Artificial Intelligence Nanodegree¶
Convolutional Neural Networks¶
Project: Write an Algorithm for a Dog Identification App¶
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", "File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.
Why We're Here¶
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
The Road Ahead¶
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
- Step 0: Import Datasets
- Step 1: Detect Humans
- Step 2: Detect Dogs
- Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
- Step 4: Use a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 6: Write your Algorithm
- Step 7: Test Your Algorithm
Step 0: Import Datasets¶
Import Dog Dataset¶
In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the load_files function from the scikit-learn library:
train_files,valid_files,test_files- numpy arrays containing file paths to imagestrain_targets,valid_targets,test_targets- numpy arrays containing onehot-encoded classification labelsdog_names- list of string-valued dog breed names for translating labels
In [1]:
from sklearn.datasets import load_files
from keras.utils import np_utils
import numpy as np
from glob import glob
# define function to load train, test, and validation datasets
def load_dataset(path):
data = load_files(path)
dog_files = np.array(data['filenames'])
dog_targets = np_utils.to_categorical(np.array(data['target']), 133)
return dog_files, dog_targets
# load train, test, and validation datasets
train_files, train_targets = load_dataset('dogImages/train')
valid_files, valid_targets = load_dataset('dogImages/valid')
test_files, test_targets = load_dataset('dogImages/test')
# load list of dog names
dog_names = [item[20:-1] for item in sorted(glob("dogImages/train/*/"))]
# print statistics about the dataset
print('There are %d total dog categories.' % len(dog_names))
print('There are %s total dog images.\n' % len(np.hstack([train_files, valid_files, test_files])))
print('There are %d training dog images.' % len(train_files))
print('There are %d validation dog images.' % len(valid_files))
print('There are %d test dog images.'% len(test_files))
Using TensorFlow backend.
There are 133 total dog categories. There are 8351 total dog images. There are 6680 training dog images. There are 835 validation dog images. There are 836 test dog images.
Import Human Dataset¶
In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array human_files.
In [2]:
import random
random.seed(8675309)
# load filenames in shuffled human dataset
human_files = np.array(glob("lfw/*/*"))
random.shuffle(human_files)
# print statistics about the dataset
print('There are %d total human images.' % len(human_files))
There are 13233 total human images.
Step 1: Detect Humans¶
We use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory.
In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
In [3]:
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[3])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
Write a Human Face Detector¶
We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.
In [4]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
(IMPLEMENTATION) Assess the Human Face Detector¶
Question 1: Use the code cell below to test the performance of the face_detector function.
- What percentage of the first 100 images in
human_fileshave a detected human face? - What percentage of the first 100 images in
dog_fileshave a detected human face?
Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.
Answer:
- 99% effectiveness on human files
- 11% false positive on dog files
Sample of 11 false negatives (human faces not recognized) and false positives (dog files resembling human face) is also shown below. Only one of them has real human face. Several dog faces are being discovered, and there are also several flukes of very small image parts that are incorrectly interpreted as human face. That said, the strengrh of the algorithm is that it has low false negative rate (only one example illustrated below where face is not appearing in frontal view), hence it could be leveraged to at least work on humans for detecting face region, though not really for accurate human/non-human classification.
In [5]:
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
face_detection_results_humans = (int(face_detector(human_files_short[i])) for i in range(len(human_files_short)))
face_detection_results_dogs = (int(face_detector(dog_files_short[i])) for i in range(len(dog_files_short)))
print ("Number of faces detected in human files:", sum(face_detection_results_humans))
print ("Number of faces detected in dog files:", sum(face_detection_results_dogs))
Number of faces detected in human files: 99 Number of faces detected in dog files: 11
In [6]:
# Show human files where face was not found
for i, image_path in enumerate(human_files_short):
if face_detector(image_path)==False:
# load color (BGR) image
img = cv2.imread(image_path)
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
# Show dog files where face was found
for i, image_path in enumerate(dog_files_short):
if face_detector(image_path):
# load color (BGR) image
img = cv2.imread(image_path)
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Question 2: This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?
Answer:
This request looks reasonable.
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on each of the datasets.
In [ ]:
## (Optional) TODO: Report the performance of another
## face detection algorithm on the LFW dataset
### Feel free to use as many code cells as needed.
Step 2: Detect Dogs¶
In this section, we use a pre-trained ResNet-50 model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.
In [5]:
from keras.applications.resnet50 import ResNet50
# define ResNet50 model
ResNet50_model = ResNet50(weights='imagenet')
Pre-process the Data¶
When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape
$$ (\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), $$where nb_samples corresponds to the total number of images (or samples), and rows, columns, and channels correspond to the number of rows, columns, and channels for each image, respectively.
The path_to_tensor function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape
The paths_to_tensor function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape
Here, nb_samples is the number of samples, or number of images, in the supplied array of image paths. It is best to think of nb_samples as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!
In [6]:
from keras.preprocessing import image
from PIL import Image
from tqdm import tqdm
def path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
img = image.load_img(img_path, target_size=(224, 224))
# convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)
x = image.img_to_array(img)
# convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor
return np.expand_dims(x, axis=0)
def paths_to_tensor(img_paths):
list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]
return np.vstack(list_of_tensors)
Making Predictions with ResNet-50¶
Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function preprocess_input. If you're curious, you can check the code for preprocess_input here.
Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the predict method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the ResNet50_predict_labels function below.
By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this dictionary.
In [7]:
from keras.applications.resnet50 import preprocess_input, decode_predictions
def ResNet50_predict_labels(img_path):
# returns prediction vector for image located at img_path
img = preprocess_input(path_to_tensor(img_path))
return np.argmax(ResNet50_model.predict(img))
Write a Dog Detector¶
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the ResNet50_predict_labels function above returns a value between 151 and 268 (inclusive).
We use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).
In [8]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
prediction = ResNet50_predict_labels(img_path)
return ((prediction <= 268) & (prediction >= 151))
(IMPLEMENTATION) Assess the Dog Detector¶
Question 3: Use the code cell below to test the performance of your dog_detector function.
- What percentage of the images in
human_files_shorthave a detected dog? - What percentage of the images in
dog_files_shorthave a detected dog?
Answer: The performance of dog detection algorithm is very good:
- Only one human face is interpreted as a dog
- 100% of the first 100 dog images are correctly identified as dogs
In [9]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
human_files_short = human_files[:100]
dog_files_short = train_files[:100]
# Do NOT modify the code above this line.
dog_detection_results_humans = (int(dog_detector(human_files_short[i])) for i in range(len(human_files_short)))
dog_detection_results_dogs = (int(dog_detector(dog_files_short[i])) for i in range(len(dog_files_short)))
print ("Number of dogs detected in human files:", sum(dog_detection_results_humans))
print ("Number of dogs detected in dog files:", sum(dog_detection_results_dogs))
Number of dogs detected in human files: 1 Number of dogs detected in dog files: 100
In [10]:
#TODO - visualize images of humans that are interpreted as dogs
# Show human files interpreted as dogs
for i, image_path in enumerate(human_files_short):
if dog_detector(image_path)==True:
# load color (BGR) image
img = cv2.imread(image_path)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image
plt.imshow(cv_rgb)
plt.show()
Step 3: Create a CNN to Classify Dog Breeds (from Scratch)¶
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
 |
 |
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
 |
 |
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
 |
 |
 |
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
Pre-process the Data¶
We rescale the images by dividing every pixel in every image by 255.
In [11]:
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
# pre-process the data for Keras
train_tensors = paths_to_tensor(train_files).astype('float32')/255
valid_tensors = paths_to_tensor(valid_files).astype('float32')/255
test_tensors = paths_to_tensor(test_files).astype('float32')/255
100%|██████████| 6680/6680 [01:37<00:00, 68.60it/s] 100%|██████████| 835/835 [00:16<00:00, 51.46it/s] 100%|██████████| 836/836 [00:21<00:00, 39.64it/s]
(IMPLEMENTATION) Model Architecture¶
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
model.summary()
We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.
Answer:
- The input to the first layer would be the original image with dimension of 224 times 224 supplied over 3 color channels
- Let's leverage convolutional neuron network infrastructure with several 2 dimensional convolutional layers with 'relative linear unit' activation function (that is believed to reduces the vanishing gradient problem which negatively impacts learning speed for the early network layers). We'll pick convolutional layers to leverage a 2-by-2 square, so they could naturally notice edges and more generally changes in patterns (both vertically and horizontally)
- Let's place max-pooling layers in between convolutional layers, and allow an exponentially growing number of filters for convolutional layers, so as to encode existence of the pattern in an area being processed, and allowing the system to forget its precise location
- Also let's add dropout layer after each max pooling layer, which would randomly remove each of these nodes with 20% likelihood - this should help reduce over-fitting and is a well-known regularization technique for neural networks
- Using a dense layer with 133 output node and softmax activation function to represent probability of a given dog breed (133)
In [14]:
from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### TODO: Define your architecture.
model.add(Conv2D(filters=4, kernel_size=2, padding='same', activation='relu', input_shape=(224, 224, 3)))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(rate=0.2, seed = 123))
model.add(Conv2D(filters=8, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(rate=0.2, seed = 123))
model.add(Conv2D(filters=16, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(rate=0.2, seed = 123))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(rate=0.2, seed = 123))
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(rate=0.2, seed = 123))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(133, activation='softmax'))
model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= conv2d_1 (Conv2D) (None, 224, 224, 4) 52 _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 112, 112, 4) 0 _________________________________________________________________ dropout_1 (Dropout) (None, 112, 112, 4) 0 _________________________________________________________________ conv2d_2 (Conv2D) (None, 112, 112, 8) 136 _________________________________________________________________ max_pooling2d_3 (MaxPooling2 (None, 56, 56, 8) 0 _________________________________________________________________ dropout_2 (Dropout) (None, 56, 56, 8) 0 _________________________________________________________________ conv2d_3 (Conv2D) (None, 56, 56, 16) 528 _________________________________________________________________ max_pooling2d_4 (MaxPooling2 (None, 28, 28, 16) 0 _________________________________________________________________ dropout_3 (Dropout) (None, 28, 28, 16) 0 _________________________________________________________________ conv2d_4 (Conv2D) (None, 28, 28, 32) 2080 _________________________________________________________________ max_pooling2d_5 (MaxPooling2 (None, 14, 14, 32) 0 _________________________________________________________________ dropout_4 (Dropout) (None, 14, 14, 32) 0 _________________________________________________________________ conv2d_5 (Conv2D) (None, 14, 14, 64) 8256 _________________________________________________________________ max_pooling2d_6 (MaxPooling2 (None, 7, 7, 64) 0 _________________________________________________________________ dropout_5 (Dropout) (None, 7, 7, 64) 0 _________________________________________________________________ flatten_2 (Flatten) (None, 3136) 0 _________________________________________________________________ dense_1 (Dense) (None, 64) 200768 _________________________________________________________________ dense_2 (Dense) (None, 133) 8645 ================================================================= Total params: 220,465 Trainable params: 220,465 Non-trainable params: 0 _________________________________________________________________
Compile the Model¶
In [54]:
# Define Neuron Network objective loss(error) function and optimizer type
# other consideration for optimizer are
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
(IMPLEMENTATION) Train the Model¶
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
In [53]:
from keras.callbacks import ModelCheckpoint
### TODO: specify the number of epochs that you would like to use to train the model.
epochs = 200
### Do NOT modify the code below this line.
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5',
verbose=1, save_best_only=True)
model.fit(train_tensors, train_targets,
validation_data=(valid_tensors, valid_targets),
epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples Epoch 1/200 4120/6680 [=================>............] - ETA: 161s - loss: 4.9137 - acc: 0.050 - ETA: 149s - loss: 4.8398 - acc: 0.025 - ETA: 145s - loss: 4.7909 - acc: 0.016 - ETA: 143s - loss: 4.7517 - acc: 0.037 - ETA: 142s - loss: 4.7688 - acc: 0.030 - ETA: 140s - loss: 4.7447 - acc: 0.033 - ETA: 139s - loss: 4.7034 - acc: 0.035 - ETA: 138s - loss: 4.7000 - acc: 0.031 - ETA: 138s - loss: 4.7099 - acc: 0.027 - ETA: 138s - loss: 4.7155 - acc: 0.025 - ETA: 137s - loss: 4.7240 - acc: 0.022 - ETA: 136s - loss: 4.7311 - acc: 0.020 - ETA: 136s - loss: 4.7426 - acc: 0.019 - ETA: 136s - loss: 4.7417 - acc: 0.017 - ETA: 135s - loss: 4.7474 - acc: 0.016 - ETA: 135s - loss: 4.7633 - acc: 0.015 - ETA: 135s - loss: 4.7660 - acc: 0.017 - ETA: 134s - loss: 4.7615 - acc: 0.022 - ETA: 134s - loss: 4.7635 - acc: 0.021 - ETA: 134s - loss: 4.7720 - acc: 0.020 - ETA: 133s - loss: 4.7735 - acc: 0.019 - ETA: 133s - loss: 4.7750 - acc: 0.018 - ETA: 133s - loss: 4.7787 - acc: 0.017 - ETA: 132s - loss: 4.7782 - acc: 0.016 - ETA: 132s - loss: 4.7765 - acc: 0.016 - ETA: 132s - loss: 4.7764 - acc: 0.015 - ETA: 131s - loss: 4.7732 - acc: 0.016 - ETA: 131s - loss: 4.7703 - acc: 0.019 - ETA: 131s - loss: 4.7732 - acc: 0.019 - ETA: 130s - loss: 4.7695 - acc: 0.020 - ETA: 130s - loss: 4.7632 - acc: 0.019 - ETA: 130s - loss: 4.7594 - acc: 0.018 - ETA: 129s - loss: 4.7615 - acc: 0.018 - ETA: 129s - loss: 4.7594 - acc: 0.019 - ETA: 129s - loss: 4.7596 - acc: 0.018 - ETA: 128s - loss: 4.7597 - acc: 0.018 - ETA: 128s - loss: 4.7560 - acc: 0.020 - ETA: 128s - loss: 4.7570 - acc: 0.019 - ETA: 128s - loss: 4.7477 - acc: 0.021 - ETA: 128s - loss: 4.7456 - acc: 0.023 - ETA: 127s - loss: 4.7549 - acc: 0.023 - ETA: 128s - loss: 4.7576 - acc: 0.022 - ETA: 129s - loss: 4.7576 - acc: 0.023 - ETA: 129s - loss: 4.7589 - acc: 0.022 - ETA: 129s - loss: 4.7596 - acc: 0.022 - ETA: 129s - loss: 4.7607 - acc: 0.021 - ETA: 128s - loss: 4.7621 - acc: 0.021 - ETA: 128s - loss: 4.7607 - 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acc: 0.02 - ETA: 15s - loss: 4.7061 - acc: 0.02 - ETA: 15s - loss: 4.7073 - acc: 0.02 - ETA: 14s - loss: 4.7069 - acc: 0.02 - ETA: 13s - loss: 4.7067 - acc: 0.02 - ETA: 13s - loss: 4.7073 - acc: 0.02 - ETA: 12s - loss: 4.7078 - acc: 0.02 - ETA: 12s - loss: 4.7079 - acc: 0.02 - ETA: 11s - loss: 4.7076 - acc: 0.02 - ETA: 10s - loss: 4.7074 - acc: 0.02 - ETA: 10s - loss: 4.7068 - acc: 0.02 - ETA: 9s - loss: 4.7066 - acc: 0.0222 - ETA: 9s - loss: 4.7064 - acc: 0.022 - ETA: 8s - loss: 4.7062 - acc: 0.022 - ETA: 7s - loss: 4.7057 - acc: 0.022 - ETA: 7s - loss: 4.7064 - acc: 0.022 - ETA: 6s - loss: 4.7066 - acc: 0.022 - ETA: 6s - loss: 4.7070 - acc: 0.021 - ETA: 5s - loss: 4.7071 - acc: 0.021 - ETA: 4s - loss: 4.7072 - acc: 0.021 - ETA: 4s - loss: 4.7069 - acc: 0.021 - ETA: 3s - loss: 4.7070 - acc: 0.021 - ETA: 3s - loss: 4.7068 - acc: 0.021 - ETA: 2s - loss: 4.7060 - acc: 0.021 - ETA: 1s - loss: 4.7059 - acc: 0.021 - ETA: 1s - loss: 4.7052 - acc: 0.021 - ETA: 0s - loss: 4.7052 - acc: 0.0218Epoch 00000: val_loss improved from inf to 4.75325, saving model to saved_models/weights.best.from_scratch.hdf5 6680/6680 [==============================] - 217s - loss: 4.7056 - acc: 0.0219 - val_loss: 4.7532 - val_acc: 0.0216 Epoch 2/200 4100/6680 [=================>............] - ETA: 230s - loss: 4.6169 - acc: 0.0000e+0 - ETA: 245s - loss: 4.6428 - acc: 0.0000e+0 - ETA: 223s - loss: 4.6617 - acc: 0.0000e+0 - ETA: 212s - loss: 4.5877 - acc: 0.0125 - ETA: 223s - loss: 4.5727 - acc: 0.020 - ETA: 215s - loss: 4.5973 - acc: 0.016 - ETA: 210s - loss: 4.6196 - acc: 0.014 - ETA: 207s - loss: 4.6127 - acc: 0.012 - ETA: 205s - loss: 4.6057 - acc: 0.022 - ETA: 203s - loss: 4.5955 - acc: 0.025 - ETA: 199s - loss: 4.5916 - acc: 0.022 - ETA: 197s - loss: 4.5811 - acc: 0.025 - ETA: 194s - loss: 4.5765 - acc: 0.026 - ETA: 192s - loss: 4.5769 - acc: 0.028 - ETA: 191s - loss: 4.5864 - acc: 0.030 - ETA: 189s - loss: 4.5935 - acc: 0.028 - ETA: 189s - loss: 4.6140 - acc: 0.026 - ETA: 189s - loss: 4.6139 - acc: 0.025 - ETA: 189s - loss: 4.6073 - acc: 0.026 - ETA: 188s - loss: 4.6037 - acc: 0.027 - ETA: 186s - loss: 4.6005 - acc: 0.028 - ETA: 188s - loss: 4.5846 - 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acc: 0.06 - ETA: 12s - loss: 4.2283 - acc: 0.06 - ETA: 11s - loss: 4.2275 - acc: 0.06 - ETA: 11s - loss: 4.2277 - acc: 0.06 - ETA: 10s - loss: 4.2274 - acc: 0.06 - ETA: 9s - loss: 4.2272 - acc: 0.0646 - ETA: 9s - loss: 4.2275 - acc: 0.064 - ETA: 8s - loss: 4.2279 - acc: 0.064 - ETA: 7s - loss: 4.2284 - acc: 0.064 - ETA: 7s - loss: 4.2279 - acc: 0.064 - ETA: 6s - loss: 4.2288 - acc: 0.064 - ETA: 5s - loss: 4.2287 - acc: 0.064 - ETA: 5s - loss: 4.2277 - acc: 0.064 - ETA: 4s - loss: 4.2276 - acc: 0.064 - ETA: 3s - loss: 4.2279 - acc: 0.064 - ETA: 3s - loss: 4.2279 - acc: 0.064 - ETA: 2s - loss: 4.2263 - acc: 0.065 - ETA: 1s - loss: 4.2269 - acc: 0.065 - ETA: 1s - loss: 4.2263 - acc: 0.064 - ETA: 0s - loss: 4.2257 - acc: 0.0649Epoch 00003: val_loss improved from 4.67102 to 4.64833, saving model to saved_models/weights.best.from_scratch.hdf5 6680/6680 [==============================] - 236s - loss: 4.2255 - acc: 0.0648 - val_loss: 4.6483 - val_acc: 0.0287 Epoch 5/200 4120/6680 [=================>............] - 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acc: 0.214 - ETA: 6s - loss: 3.2289 - acc: 0.214 - ETA: 5s - loss: 3.2301 - acc: 0.214 - ETA: 4s - loss: 3.2300 - acc: 0.214 - ETA: 4s - loss: 3.2282 - acc: 0.214 - ETA: 3s - loss: 3.2276 - acc: 0.214 - ETA: 2s - loss: 3.2273 - acc: 0.214 - ETA: 2s - loss: 3.2277 - acc: 0.214 - ETA: 1s - loss: 3.2275 - acc: 0.215 - ETA: 0s - loss: 3.2265 - acc: 0.2155Epoch 00014: val_loss did not improve 6680/6680 [==============================] - 235s - loss: 3.2275 - acc: 0.2153 - val_loss: 4.3812 - val_acc: 0.0647 Epoch 16/200 4120/6680 [=================>............] - ETA: 207s - loss: 2.8654 - acc: 0.250 - ETA: 201s - loss: 2.9698 - acc: 0.200 - ETA: 199s - loss: 3.2081 - acc: 0.150 - ETA: 201s - loss: 3.2746 - acc: 0.162 - ETA: 201s - loss: 3.1909 - acc: 0.180 - ETA: 200s - loss: 3.2222 - acc: 0.166 - ETA: 198s - loss: 3.1974 - acc: 0.178 - ETA: 197s - loss: 3.2147 - acc: 0.175 - ETA: 198s - loss: 3.2017 - acc: 0.183 - ETA: 197s - loss: 3.1648 - acc: 0.190 - ETA: 196s - loss: 3.1804 - acc: 0.200 - 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ETA: 137s - loss: 3.0762 - acc: 0.243 - ETA: 136s - loss: 3.0722 - acc: 0.243 - ETA: 136s - loss: 3.0746 - acc: 0.243 - ETA: 135s - loss: 3.0754 - acc: 0.242 - ETA: 135s - loss: 3.0688 - acc: 0.244 - ETA: 134s - loss: 3.0678 - acc: 0.245 - ETA: 133s - loss: 3.0608 - acc: 0.247 - ETA: 133s - loss: 3.0613 - acc: 0.247 - ETA: 132s - loss: 3.0651 - acc: 0.246 - ETA: 131s - loss: 3.0641 - acc: 0.246 - ETA: 131s - loss: 3.0639 - acc: 0.245 - ETA: 130s - loss: 3.0666 - acc: 0.244 - ETA: 130s - loss: 3.0692 - acc: 0.244 - ETA: 129s - loss: 3.0717 - acc: 0.244 - ETA: 129s - loss: 3.0718 - acc: 0.243 - ETA: 128s - loss: 3.0650 - acc: 0.244 - ETA: 128s - loss: 3.0634 - acc: 0.245 - ETA: 128s - loss: 3.0674 - acc: 0.245 - ETA: 128s - loss: 3.0678 - acc: 0.245 - ETA: 127s - loss: 3.0677 - acc: 0.244 - ETA: 127s - loss: 3.0672 - acc: 0.245 - ETA: 127s - loss: 3.0630 - acc: 0.247 - ETA: 126s - loss: 3.0671 - acc: 0.246 - ETA: 125s - loss: 3.0675 - acc: 0.247 - ETA: 125s - loss: 3.0694 - acc: 0.246 - 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acc: 0.23 - ETA: 93s - loss: 3.1011 - acc: 0.23 - ETA: 93s - loss: 3.1014 - acc: 0.23 - ETA: 92s - loss: 3.1036 - acc: 0.23 - ETA: 91s - loss: 3.1044 - acc: 0.23 - ETA: 91s - loss: 3.1043 - acc: 0.23 - ETA: 90s - loss: 3.1042 - acc: 0.23 - ETA: 90s - loss: 3.1037 - acc: 0.23 - ETA: 89s - loss: 3.1054 - acc: 0.23 - ETA: 88s - loss: 3.1045 - acc: 0.23 - ETA: 88s - loss: 3.1057 - acc: 0.23 - ETA: 87s - loss: 3.1085 - acc: 0.23 - ETA: 86s - loss: 3.1118 - acc: 0.23 - ETA: 86s - loss: 3.1091 - acc: 0.23 - ETA: 85s - loss: 3.1081 - acc: 0.23 - ETA: 85s - loss: 3.1092 - acc: 0.23 - ETA: 84s - loss: 3.1090 - acc: 0.23 - ETA: 83s - loss: 3.1090 - acc: 0.23 - ETA: 83s - loss: 3.1121 - acc: 0.23 - ETA: 82s - loss: 3.1132 - acc: 0.23886660/6680 [============================>.] - ETA: 81s - loss: 3.1148 - acc: 0.23 - ETA: 81s - loss: 3.1166 - acc: 0.23 - ETA: 80s - loss: 3.1161 - acc: 0.23 - ETA: 79s - loss: 3.1175 - acc: 0.23 - ETA: 79s - loss: 3.1172 - acc: 0.23 - ETA: 78s - loss: 3.1159 - acc: 0.23 - 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acc: 0.24 - ETA: 26s - loss: 3.1401 - acc: 0.24 - ETA: 25s - loss: 3.1390 - acc: 0.24 - ETA: 25s - loss: 3.1402 - acc: 0.24 - ETA: 24s - loss: 3.1382 - acc: 0.24 - ETA: 23s - loss: 3.1372 - acc: 0.24 - ETA: 23s - loss: 3.1348 - acc: 0.24 - ETA: 22s - loss: 3.1367 - acc: 0.24 - ETA: 21s - loss: 3.1380 - acc: 0.24 - ETA: 21s - loss: 3.1394 - acc: 0.24 - ETA: 20s - loss: 3.1398 - acc: 0.24 - ETA: 20s - loss: 3.1395 - acc: 0.24 - ETA: 19s - loss: 3.1394 - acc: 0.24 - ETA: 18s - loss: 3.1424 - acc: 0.24 - ETA: 18s - loss: 3.1420 - acc: 0.24 - ETA: 17s - loss: 3.1420 - acc: 0.24 - ETA: 16s - loss: 3.1425 - acc: 0.24 - ETA: 16s - loss: 3.1421 - acc: 0.24 - ETA: 15s - loss: 3.1420 - acc: 0.24 - ETA: 14s - loss: 3.1441 - acc: 0.23 - ETA: 14s - loss: 3.1440 - acc: 0.24 - ETA: 13s - loss: 3.1432 - acc: 0.23 - ETA: 13s - loss: 3.1440 - acc: 0.23 - ETA: 12s - loss: 3.1438 - acc: 0.23 - ETA: 11s - loss: 3.1447 - acc: 0.23 - ETA: 11s - loss: 3.1441 - acc: 0.23 - ETA: 10s - loss: 3.1451 - acc: 0.23 - ETA: 9s - loss: 3.1441 - acc: 0.2390 - ETA: 9s - loss: 3.1450 - acc: 0.239 - ETA: 8s - loss: 3.1450 - acc: 0.238 - ETA: 7s - loss: 3.1464 - acc: 0.238 - ETA: 7s - loss: 3.1460 - acc: 0.238 - ETA: 6s - loss: 3.1478 - acc: 0.238 - ETA: 5s - loss: 3.1477 - acc: 0.238 - ETA: 5s - loss: 3.1489 - acc: 0.238 - ETA: 4s - loss: 3.1477 - acc: 0.239 - ETA: 3s - loss: 3.1464 - acc: 0.239 - ETA: 3s - loss: 3.1468 - acc: 0.239 - ETA: 2s - loss: 3.1470 - acc: 0.239 - ETA: 1s - loss: 3.1465 - acc: 0.239 - ETA: 1s - loss: 3.1464 - acc: 0.239 - ETA: 0s - loss: 3.1477 - acc: 0.2392Epoch 00015: val_loss improved from 4.24126 to 4.21783, saving model to saved_models/weights.best.from_scratch.hdf5 6680/6680 [==============================] - 235s - loss: 3.1464 - acc: 0.2394 - val_loss: 4.2178 - val_acc: 0.0743 Epoch 17/200 4120/6680 [=================>............] - ETA: 203s - loss: 3.1516 - acc: 0.200 - ETA: 197s - loss: 3.3462 - acc: 0.200 - ETA: 194s - loss: 3.2383 - acc: 0.233 - ETA: 193s - loss: 3.0543 - acc: 0.237 - ETA: 191s - loss: 3.0195 - acc: 0.240 - ETA: 190s - loss: 3.0103 - acc: 0.250 - ETA: 192s - loss: 2.9743 - acc: 0.250 - ETA: 191s - loss: 2.9614 - acc: 0.250 - ETA: 191s - loss: 2.9763 - acc: 0.255 - ETA: 190s - loss: 2.9638 - acc: 0.260 - ETA: 189s - loss: 2.9361 - acc: 0.281 - ETA: 188s - loss: 2.9329 - acc: 0.266 - ETA: 188s - loss: 2.9558 - acc: 0.273 - ETA: 188s - loss: 2.9887 - acc: 0.257 - ETA: 188s - loss: 3.0390 - acc: 0.253 - ETA: 188s - loss: 3.0159 - acc: 0.256 - ETA: 187s - loss: 2.9867 - acc: 0.264 - ETA: 186s - loss: 2.9857 - acc: 0.263 - ETA: 186s - loss: 2.9749 - acc: 0.268 - ETA: 185s - loss: 2.9639 - acc: 0.265 - ETA: 184s - loss: 2.9414 - acc: 0.271 - ETA: 184s - loss: 2.9412 - acc: 0.272 - ETA: 183s - loss: 2.9573 - acc: 0.269 - ETA: 182s - loss: 2.9276 - acc: 0.279 - ETA: 182s - loss: 2.9223 - acc: 0.278 - ETA: 182s - loss: 2.9198 - acc: 0.276 - ETA: 181s - loss: 2.9314 - acc: 0.272 - ETA: 181s - loss: 2.9126 - acc: 0.276 - ETA: 180s - loss: 2.9056 - 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ETA: 99s - loss: 3.0179 - acc: 0.25 - ETA: 99s - loss: 3.0174 - acc: 0.25 - ETA: 98s - loss: 3.0171 - acc: 0.25 - ETA: 97s - loss: 3.0148 - acc: 0.25 - ETA: 97s - loss: 3.0156 - acc: 0.25 - ETA: 96s - loss: 3.0178 - acc: 0.25 - ETA: 95s - loss: 3.0145 - acc: 0.25 - ETA: 95s - loss: 3.0149 - acc: 0.25 - ETA: 94s - loss: 3.0139 - acc: 0.25 - ETA: 94s - loss: 3.0157 - acc: 0.25 - ETA: 93s - loss: 3.0181 - acc: 0.25 - ETA: 92s - loss: 3.0187 - acc: 0.25 - ETA: 92s - loss: 3.0141 - acc: 0.25 - ETA: 91s - loss: 3.0156 - acc: 0.25 - ETA: 91s - loss: 3.0189 - acc: 0.25 - ETA: 90s - loss: 3.0190 - acc: 0.25 - ETA: 90s - loss: 3.0174 - acc: 0.25 - ETA: 89s - loss: 3.0202 - acc: 0.25 - ETA: 89s - loss: 3.0191 - acc: 0.25 - ETA: 88s - loss: 3.0183 - acc: 0.25 - ETA: 87s - loss: 3.0199 - acc: 0.25 - ETA: 87s - loss: 3.0227 - acc: 0.25 - ETA: 86s - loss: 3.0230 - acc: 0.25 - ETA: 85s - loss: 3.0240 - acc: 0.25 - ETA: 85s - loss: 3.0274 - acc: 0.25 - ETA: 84s - loss: 3.0321 - acc: 0.25 - ETA: 84s - loss: 3.0304 - acc: 0.25176660/6680 [============================>.] - ETA: 83s - loss: 3.0307 - acc: 0.25 - ETA: 83s - loss: 3.0289 - acc: 0.25 - ETA: 82s - loss: 3.0258 - acc: 0.25 - ETA: 82s - loss: 3.0265 - acc: 0.25 - ETA: 81s - loss: 3.0288 - acc: 0.25 - ETA: 81s - loss: 3.0283 - acc: 0.25 - ETA: 80s - loss: 3.0262 - acc: 0.25 - ETA: 79s - loss: 3.0278 - acc: 0.24 - ETA: 79s - loss: 3.0283 - acc: 0.24 - ETA: 78s - loss: 3.0324 - acc: 0.24 - ETA: 77s - loss: 3.0345 - acc: 0.24 - ETA: 76s - loss: 3.0354 - acc: 0.24 - ETA: 76s - loss: 3.0345 - acc: 0.25 - ETA: 75s - loss: 3.0317 - acc: 0.25 - ETA: 74s - loss: 3.0321 - acc: 0.25 - ETA: 74s - loss: 3.0324 - acc: 0.25 - ETA: 73s - loss: 3.0324 - acc: 0.25 - ETA: 73s - loss: 3.0332 - acc: 0.25 - ETA: 72s - loss: 3.0315 - acc: 0.25 - ETA: 71s - loss: 3.0304 - acc: 0.25 - ETA: 71s - loss: 3.0285 - acc: 0.25 - ETA: 70s - loss: 3.0280 - acc: 0.25 - ETA: 69s - loss: 3.0275 - acc: 0.25 - ETA: 69s - loss: 3.0287 - acc: 0.25 - ETA: 68s - loss: 3.0276 - acc: 0.25 - 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acc: 0.24 - ETA: 14s - loss: 3.0638 - acc: 0.24 - ETA: 14s - loss: 3.0653 - acc: 0.24 - ETA: 13s - loss: 3.0670 - acc: 0.24 - ETA: 12s - loss: 3.0688 - acc: 0.24 - ETA: 12s - loss: 3.0680 - acc: 0.24 - ETA: 11s - loss: 3.0689 - acc: 0.24 - ETA: 10s - loss: 3.0679 - acc: 0.24 - ETA: 10s - loss: 3.0661 - acc: 0.24 - ETA: 9s - loss: 3.0657 - acc: 0.2470 - ETA: 8s - loss: 3.0642 - acc: 0.247 - ETA: 8s - loss: 3.0638 - acc: 0.247 - ETA: 7s - loss: 3.0647 - acc: 0.247 - ETA: 6s - loss: 3.0668 - acc: 0.246 - ETA: 6s - loss: 3.0671 - acc: 0.246 - ETA: 5s - loss: 3.0674 - acc: 0.246 - ETA: 4s - loss: 3.0677 - acc: 0.246 - ETA: 4s - loss: 3.0687 - acc: 0.246 - ETA: 3s - loss: 3.0687 - acc: 0.245 - ETA: 2s - loss: 3.0687 - acc: 0.246 - ETA: 2s - loss: 3.0696 - acc: 0.245 - ETA: 1s - loss: 3.0693 - acc: 0.246 - ETA: 0s - loss: 3.0683 - acc: 0.2462Epoch 00016: val_loss did not improve 6680/6680 [==============================] - 240s - loss: 3.0670 - acc: 0.2467 - val_loss: 4.4298 - val_acc: 0.0467 Epoch 18/200 4100/6680 [=================>............] - ETA: 202s - loss: 2.5031 - acc: 0.250 - ETA: 211s - loss: 2.5986 - acc: 0.275 - ETA: 215s - loss: 2.5784 - acc: 0.250 - ETA: 229s - loss: 2.6546 - acc: 0.262 - ETA: 227s - loss: 2.9620 - acc: 0.240 - ETA: 223s - loss: 2.9788 - acc: 0.241 - ETA: 221s - loss: 2.9414 - acc: 0.228 - ETA: 219s - loss: 2.9008 - acc: 0.256 - ETA: 225s - loss: 2.9302 - acc: 0.261 - ETA: 223s - loss: 2.9292 - acc: 0.265 - ETA: 222s - loss: 2.9326 - acc: 0.281 - ETA: 222s - loss: 2.9304 - acc: 0.287 - ETA: 219s - loss: 2.9037 - acc: 0.284 - ETA: 218s - loss: 2.8744 - acc: 0.296 - ETA: 221s - loss: 2.9000 - acc: 0.290 - ETA: 218s - loss: 2.8830 - acc: 0.290 - ETA: 216s - loss: 2.8526 - acc: 0.302 - ETA: 215s - loss: 2.8501 - acc: 0.297 - ETA: 213s - loss: 2.8459 - acc: 0.302 - ETA: 211s - loss: 2.8658 - acc: 0.297 - ETA: 209s - loss: 2.8754 - acc: 0.295 - ETA: 209s - loss: 2.8723 - acc: 0.293 - ETA: 207s - loss: 2.8680 - acc: 0.293 - ETA: 208s - loss: 2.8717 - acc: 0.291 - ETA: 207s - loss: 2.8653 - acc: 0.290 - 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acc: 0.2665Epoch 00017: val_loss improved from 4.21783 to 4.20930, saving model to saved_models/weights.best.from_scratch.hdf5 6680/6680 [==============================] - 239s - loss: 2.9829 - acc: 0.2662 - val_loss: 4.2093 - val_acc: 0.0766 Epoch 19/200 4120/6680 [=================>............] - ETA: 196s - loss: 3.2508 - acc: 0.200 - ETA: 235s - loss: 2.8558 - acc: 0.275 - ETA: 222s - loss: 2.8025 - acc: 0.266 - ETA: 218s - loss: 2.9323 - acc: 0.262 - ETA: 218s - loss: 2.8943 - acc: 0.290 - ETA: 213s - loss: 2.9896 - acc: 0.266 - ETA: 208s - loss: 2.9447 - acc: 0.257 - ETA: 206s - loss: 2.9538 - acc: 0.256 - ETA: 203s - loss: 2.9375 - acc: 0.244 - ETA: 201s - loss: 2.9143 - acc: 0.245 - ETA: 199s - loss: 2.8938 - acc: 0.250 - ETA: 197s - loss: 2.9046 - acc: 0.250 - ETA: 196s - loss: 2.8943 - acc: 0.250 - ETA: 195s - loss: 2.8787 - acc: 0.264 - ETA: 194s - loss: 2.8545 - acc: 0.266 - ETA: 192s - loss: 2.8627 - acc: 0.262 - ETA: 191s - loss: 2.8552 - acc: 0.273 - ETA: 190s - loss: 2.8693 - 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228s - loss: 2.6317 - acc: 0.3322 - val_loss: 4.2189 - val_acc: 0.0814 Epoch 23/200 4120/6680 [=================>............] - ETA: 199s - loss: 2.5434 - acc: 0.250 - ETA: 238s - loss: 2.3041 - acc: 0.375 - ETA: 223s - loss: 2.2803 - acc: 0.383 - ETA: 219s - loss: 2.5024 - acc: 0.375 - ETA: 216s - loss: 2.4463 - acc: 0.380 - ETA: 210s - loss: 2.4057 - acc: 0.400 - ETA: 206s - loss: 2.4584 - acc: 0.371 - ETA: 206s - loss: 2.5337 - acc: 0.362 - ETA: 202s - loss: 2.5403 - acc: 0.350 - ETA: 200s - loss: 2.5249 - acc: 0.360 - ETA: 198s - loss: 2.5361 - acc: 0.354 - ETA: 196s - loss: 2.4764 - acc: 0.370 - ETA: 199s - loss: 2.5263 - acc: 0.365 - ETA: 202s - loss: 2.5288 - acc: 0.364 - ETA: 200s - loss: 2.5672 - acc: 0.353 - ETA: 198s - loss: 2.5485 - acc: 0.362 - ETA: 196s - loss: 2.5336 - acc: 0.370 - ETA: 195s - loss: 2.5025 - acc: 0.380 - ETA: 194s - loss: 2.5157 - acc: 0.378 - ETA: 192s - loss: 2.5141 - acc: 0.375 - ETA: 191s - loss: 2.4929 - acc: 0.378 - ETA: 189s - loss: 2.5079 - acc: 0.375 - 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acc: 0.492 - ETA: 6s - loss: 1.8885 - acc: 0.492 - ETA: 6s - loss: 1.8886 - acc: 0.492 - ETA: 5s - loss: 1.8910 - acc: 0.491 - ETA: 4s - loss: 1.8896 - acc: 0.491 - ETA: 4s - loss: 1.8903 - acc: 0.492 - ETA: 3s - loss: 1.8927 - acc: 0.492 - ETA: 2s - loss: 1.8931 - acc: 0.492 - ETA: 2s - loss: 1.8941 - acc: 0.492 - ETA: 1s - loss: 1.8951 - acc: 0.492 - ETA: 0s - loss: 1.8951 - acc: 0.4919Epoch 00032: val_loss did not improve 6680/6680 [==============================] - 244s - loss: 1.8956 - acc: 0.4912 - val_loss: 4.4709 - val_acc: 0.0862 Epoch 34/200 4100/6680 [=================>............] - ETA: 187s - loss: 1.5241 - acc: 0.650 - ETA: 186s - loss: 1.9509 - acc: 0.550 - ETA: 218s - loss: 1.8546 - acc: 0.550 - ETA: 210s - loss: 1.8613 - acc: 0.525 - ETA: 206s - loss: 1.8237 - acc: 0.520 - ETA: 203s - loss: 1.8030 - acc: 0.550 - ETA: 208s - loss: 1.8133 - acc: 0.528 - ETA: 207s - loss: 1.8427 - acc: 0.518 - ETA: 204s - loss: 1.8337 - acc: 0.516 - ETA: 201s - loss: 1.8103 - acc: 0.520 - 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acc: 0.50 - ETA: 10s - loss: 1.8377 - acc: 0.50 - ETA: 10s - loss: 1.8368 - acc: 0.50 - ETA: 9s - loss: 1.8378 - acc: 0.5028 - ETA: 8s - loss: 1.8371 - acc: 0.503 - ETA: 8s - loss: 1.8385 - acc: 0.503 - ETA: 7s - loss: 1.8412 - acc: 0.502 - ETA: 6s - loss: 1.8423 - acc: 0.502 - ETA: 6s - loss: 1.8441 - acc: 0.502 - ETA: 5s - loss: 1.8447 - acc: 0.502 - ETA: 4s - loss: 1.8450 - acc: 0.502 - ETA: 4s - loss: 1.8437 - acc: 0.502 - ETA: 3s - loss: 1.8434 - acc: 0.502 - ETA: 2s - loss: 1.8425 - acc: 0.502 - ETA: 2s - loss: 1.8443 - acc: 0.502 - ETA: 1s - loss: 1.8431 - acc: 0.502 - ETA: 0s - loss: 1.8440 - acc: 0.5021Epoch 00033: val_loss did not improve 6680/6680 [==============================] - 238s - loss: 1.8445 - acc: 0.5022 - val_loss: 4.6775 - val_acc: 0.0862 Epoch 35/200 4100/6680 [=================>............] - ETA: 191s - loss: 1.4359 - acc: 0.550 - ETA: 191s - loss: 1.7447 - acc: 0.500 - ETA: 190s - loss: 1.6376 - acc: 0.533 - ETA: 188s - loss: 1.5901 - acc: 0.525 - ETA: 189s - 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ETA: 14s - loss: 1.7733 - acc: 0.51 - ETA: 14s - loss: 1.7748 - acc: 0.50 - ETA: 13s - loss: 1.7726 - acc: 0.50 - ETA: 12s - loss: 1.7742 - acc: 0.50 - ETA: 12s - loss: 1.7750 - acc: 0.50 - ETA: 11s - loss: 1.7746 - acc: 0.50 - ETA: 10s - loss: 1.7749 - acc: 0.50 - ETA: 10s - loss: 1.7747 - acc: 0.50 - ETA: 9s - loss: 1.7739 - acc: 0.5095 - ETA: 8s - loss: 1.7725 - acc: 0.510 - ETA: 8s - loss: 1.7744 - acc: 0.509 - ETA: 7s - loss: 1.7746 - acc: 0.509 - ETA: 6s - loss: 1.7774 - acc: 0.509 - ETA: 6s - loss: 1.7777 - acc: 0.508 - ETA: 5s - loss: 1.7770 - acc: 0.509 - ETA: 4s - loss: 1.7767 - acc: 0.509 - ETA: 4s - loss: 1.7767 - acc: 0.509 - ETA: 3s - loss: 1.7752 - acc: 0.509 - ETA: 2s - loss: 1.7770 - acc: 0.508 - ETA: 2s - loss: 1.7752 - acc: 0.509 - ETA: 1s - loss: 1.7770 - acc: 0.508 - ETA: 0s - loss: 1.7779 - acc: 0.5084Epoch 00034: val_loss did not improve 6680/6680 [==============================] - 239s - loss: 1.7808 - acc: 0.5079 - val_loss: 4.6454 - val_acc: 0.0743 Epoch 36/200 4100/6680 [=================>............] - ETA: 211s - loss: 1.7869 - acc: 0.650 - ETA: 207s - loss: 1.8921 - acc: 0.650 - ETA: 229s - loss: 1.7265 - acc: 0.633 - ETA: 220s - loss: 1.6806 - acc: 0.612 - ETA: 215s - loss: 1.6693 - acc: 0.620 - ETA: 211s - loss: 1.6475 - acc: 0.608 - ETA: 210s - loss: 1.6185 - acc: 0.607 - ETA: 210s - loss: 1.6566 - acc: 0.581 - ETA: 212s - loss: 1.5769 - acc: 0.594 - ETA: 210s - loss: 1.5835 - acc: 0.575 - ETA: 208s - loss: 1.5554 - acc: 0.581 - ETA: 206s - loss: 1.5377 - acc: 0.583 - ETA: 205s - loss: 1.5595 - acc: 0.580 - ETA: 203s - loss: 1.6028 - acc: 0.571 - ETA: 202s - loss: 1.5974 - acc: 0.570 - ETA: 202s - loss: 1.6228 - acc: 0.565 - ETA: 204s - loss: 1.6240 - acc: 0.564 - ETA: 203s - loss: 1.6025 - acc: 0.566 - ETA: 201s - loss: 1.5815 - acc: 0.568 - ETA: 199s - loss: 1.5763 - acc: 0.565 - ETA: 198s - loss: 1.6027 - acc: 0.552 - ETA: 196s - loss: 1.5868 - acc: 0.554 - ETA: 196s - loss: 1.5982 - acc: 0.554 - ETA: 195s - loss: 1.6307 - acc: 0.550 - ETA: 196s - loss: 1.6143 - acc: 0.556 - 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ETA: 0s - loss: 1.7135 - acc: 0.5362Epoch 00035: val_loss did not improve 6680/6680 [==============================] - 239s - loss: 1.7147 - acc: 0.5359 - val_loss: 4.8612 - val_acc: 0.0731 Epoch 37/200 4100/6680 [=================>............] - ETA: 254s - loss: 2.3505 - acc: 0.350 - ETA: 236s - loss: 1.9415 - acc: 0.400 - ETA: 236s - loss: 1.9332 - acc: 0.433 - ETA: 233s - loss: 1.8722 - acc: 0.450 - ETA: 239s - loss: 1.7301 - acc: 0.500 - ETA: 245s - loss: 1.6528 - acc: 0.525 - ETA: 240s - loss: 1.6246 - acc: 0.535 - ETA: 235s - loss: 1.6454 - acc: 0.537 - ETA: 231s - loss: 1.6373 - acc: 0.538 - ETA: 228s - loss: 1.6302 - acc: 0.535 - ETA: 224s - loss: 1.5963 - acc: 0.536 - ETA: 220s - loss: 1.5816 - acc: 0.550 - ETA: 218s - loss: 1.5459 - acc: 0.553 - ETA: 217s - loss: 1.5528 - acc: 0.542 - ETA: 218s - loss: 1.5582 - acc: 0.543 - ETA: 218s - loss: 1.5721 - acc: 0.543 - ETA: 222s - loss: 1.5511 - acc: 0.555 - ETA: 222s - loss: 1.6299 - acc: 0.541 - ETA: 219s - loss: 1.6758 - acc: 0.531 - 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235s - loss: 1.5621 - acc: 0.5681 - val_loss: 4.8864 - val_acc: 0.0814 Epoch 41/200 4120/6680 [=================>............] - ETA: 227s - loss: 1.6266 - acc: 0.600 - ETA: 219s - loss: 1.4326 - acc: 0.600 - ETA: 212s - loss: 1.5949 - acc: 0.550 - ETA: 210s - loss: 1.5978 - acc: 0.550 - ETA: 208s - loss: 1.5089 - acc: 0.570 - ETA: 208s - loss: 1.4847 - acc: 0.591 - ETA: 208s - loss: 1.4823 - acc: 0.592 - ETA: 214s - loss: 1.4393 - acc: 0.618 - ETA: 213s - loss: 1.4287 - acc: 0.633 - ETA: 212s - loss: 1.4197 - acc: 0.645 - ETA: 209s - loss: 1.4543 - acc: 0.640 - ETA: 209s - loss: 1.4264 - acc: 0.645 - ETA: 207s - loss: 1.4024 - acc: 0.650 - ETA: 214s - loss: 1.3898 - acc: 0.657 - ETA: 212s - loss: 1.3831 - acc: 0.656 - ETA: 211s - loss: 1.4490 - acc: 0.631 - ETA: 209s - loss: 1.4313 - acc: 0.635 - ETA: 207s - loss: 1.4674 - acc: 0.633 - ETA: 206s - loss: 1.4324 - acc: 0.639 - ETA: 204s - loss: 1.4313 - acc: 0.637 - ETA: 203s - loss: 1.4212 - acc: 0.635 - ETA: 201s - loss: 1.4455 - acc: 0.631 - 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acc: 0.651 - ETA: 1s - loss: 1.1755 - acc: 0.651 - ETA: 1s - loss: 1.1760 - acc: 0.651 - ETA: 0s - loss: 1.1760 - acc: 0.6520Epoch 00050: val_loss did not improve 6680/6680 [==============================] - 232s - loss: 1.1753 - acc: 0.6519 - val_loss: 5.4089 - val_acc: 0.0683 Epoch 52/200 4100/6680 [=================>............] - ETA: 198s - loss: 2.0053 - acc: 0.250 - ETA: 208s - loss: 1.5729 - acc: 0.425 - ETA: 205s - loss: 1.2912 - acc: 0.533 - ETA: 227s - loss: 1.1870 - acc: 0.575 - ETA: 228s - loss: 1.1090 - acc: 0.620 - ETA: 223s - loss: 1.0654 - acc: 0.650 - ETA: 227s - loss: 1.0657 - acc: 0.664 - ETA: 225s - loss: 1.0475 - acc: 0.668 - ETA: 222s - loss: 1.0770 - acc: 0.650 - ETA: 223s - loss: 1.0790 - acc: 0.655 - ETA: 221s - loss: 1.0626 - acc: 0.659 - ETA: 218s - loss: 1.0409 - acc: 0.662 - ETA: 216s - loss: 1.0265 - acc: 0.669 - ETA: 213s - loss: 1.0323 - acc: 0.671 - ETA: 211s - loss: 1.0189 - acc: 0.683 - ETA: 208s - loss: 1.0252 - acc: 0.684 - ETA: 205s - loss: 1.0110 - 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acc: 0.662 - ETA: 6s - loss: 1.1580 - acc: 0.662 - ETA: 5s - loss: 1.1581 - acc: 0.662 - ETA: 4s - loss: 1.1578 - acc: 0.662 - ETA: 4s - loss: 1.1574 - acc: 0.662 - ETA: 3s - loss: 1.1580 - acc: 0.661 - ETA: 2s - loss: 1.1594 - acc: 0.661 - ETA: 2s - loss: 1.1601 - acc: 0.661 - ETA: 1s - loss: 1.1588 - acc: 0.661 - ETA: 0s - loss: 1.1593 - acc: 0.6616Epoch 00051: val_loss did not improve 6680/6680 [==============================] - 245s - loss: 1.1596 - acc: 0.6614 - val_loss: 5.6004 - val_acc: 0.0659 Epoch 53/200 4120/6680 [=================>............] - ETA: 185s - loss: 0.8689 - acc: 0.750 - ETA: 196s - loss: 1.1636 - acc: 0.700 - ETA: 194s - loss: 1.0469 - acc: 0.700 - ETA: 197s - loss: 0.9419 - acc: 0.725 - ETA: 196s - loss: 0.9688 - acc: 0.710 - ETA: 194s - loss: 0.9899 - acc: 0.708 - ETA: 197s - loss: 1.0227 - acc: 0.685 - ETA: 196s - loss: 1.0851 - acc: 0.662 - ETA: 192s - loss: 1.0944 - acc: 0.666 - ETA: 190s - loss: 1.0508 - acc: 0.680 - ETA: 189s - loss: 1.0379 - acc: 0.690 - 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acc: 0.69 - ETA: 92s - loss: 1.0680 - acc: 0.69 - ETA: 91s - loss: 1.0689 - acc: 0.69 - ETA: 90s - loss: 1.0693 - acc: 0.69 - ETA: 90s - loss: 1.0700 - acc: 0.69 - ETA: 89s - loss: 1.0713 - acc: 0.69 - ETA: 89s - loss: 1.0723 - acc: 0.69 - ETA: 88s - loss: 1.0700 - acc: 0.69 - ETA: 87s - loss: 1.0697 - acc: 0.69 - ETA: 87s - loss: 1.0717 - acc: 0.69 - ETA: 86s - loss: 1.0732 - acc: 0.69 - ETA: 86s - loss: 1.0710 - acc: 0.69 - ETA: 85s - loss: 1.0715 - acc: 0.69 - ETA: 85s - loss: 1.0734 - acc: 0.69 - ETA: 84s - loss: 1.0724 - acc: 0.69 - ETA: 84s - loss: 1.0714 - acc: 0.69 - ETA: 83s - loss: 1.0723 - acc: 0.69 - ETA: 82s - loss: 1.0709 - acc: 0.69 - ETA: 82s - loss: 1.0738 - acc: 0.69 - ETA: 81s - loss: 1.0722 - acc: 0.69206660/6680 [============================>.] - ETA: 81s - loss: 1.0699 - acc: 0.69 - ETA: 80s - loss: 1.0684 - acc: 0.69 - ETA: 80s - loss: 1.0672 - acc: 0.69 - ETA: 79s - loss: 1.0680 - acc: 0.69 - ETA: 79s - loss: 1.0709 - acc: 0.69 - ETA: 78s - loss: 1.0730 - acc: 0.69 - 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acc: 0.67 - ETA: 26s - loss: 1.1258 - acc: 0.67 - ETA: 25s - loss: 1.1258 - acc: 0.67 - ETA: 25s - loss: 1.1258 - acc: 0.67 - ETA: 24s - loss: 1.1280 - acc: 0.67 - ETA: 23s - loss: 1.1282 - acc: 0.67 - ETA: 23s - loss: 1.1301 - acc: 0.67 - ETA: 22s - loss: 1.1314 - acc: 0.67 - ETA: 22s - loss: 1.1304 - acc: 0.67 - ETA: 21s - loss: 1.1303 - acc: 0.67 - ETA: 20s - loss: 1.1306 - acc: 0.67 - ETA: 20s - loss: 1.1305 - acc: 0.67 - ETA: 19s - loss: 1.1298 - acc: 0.67 - ETA: 18s - loss: 1.1331 - acc: 0.67 - ETA: 18s - loss: 1.1339 - acc: 0.67 - ETA: 17s - loss: 1.1346 - acc: 0.67 - ETA: 16s - loss: 1.1345 - acc: 0.67 - ETA: 16s - loss: 1.1351 - acc: 0.67 - ETA: 15s - loss: 1.1357 - acc: 0.67 - ETA: 14s - loss: 1.1391 - acc: 0.67 - ETA: 14s - loss: 1.1391 - acc: 0.67 - ETA: 13s - loss: 1.1387 - acc: 0.67 - ETA: 13s - loss: 1.1385 - acc: 0.67 - ETA: 12s - loss: 1.1390 - acc: 0.67 - ETA: 11s - loss: 1.1387 - acc: 0.67 - ETA: 11s - loss: 1.1398 - acc: 0.67 - ETA: 10s - loss: 1.1394 - acc: 0.67 - ETA: 9s - loss: 1.1395 - acc: 0.6759 - ETA: 9s - loss: 1.1402 - acc: 0.675 - ETA: 8s - loss: 1.1411 - acc: 0.676 - ETA: 7s - loss: 1.1436 - acc: 0.675 - ETA: 7s - loss: 1.1450 - acc: 0.675 - ETA: 6s - loss: 1.1434 - acc: 0.675 - ETA: 5s - loss: 1.1443 - acc: 0.675 - ETA: 5s - loss: 1.1460 - acc: 0.675 - ETA: 4s - loss: 1.1463 - acc: 0.675 - ETA: 3s - loss: 1.1460 - acc: 0.675 - ETA: 3s - loss: 1.1454 - acc: 0.675 - ETA: 2s - loss: 1.1462 - acc: 0.674 - ETA: 1s - loss: 1.1454 - acc: 0.674 - ETA: 1s - loss: 1.1457 - acc: 0.674 - ETA: 0s - loss: 1.1453 - acc: 0.6746Epoch 00052: val_loss did not improve 6680/6680 [==============================] - 234s - loss: 1.1455 - acc: 0.6749 - val_loss: 5.6228 - val_acc: 0.0790 Epoch 54/200 4120/6680 [=================>............] - ETA: 210s - loss: 1.6798 - acc: 0.650 - ETA: 198s - loss: 1.1060 - acc: 0.750 - ETA: 196s - loss: 0.9101 - acc: 0.766 - ETA: 195s - loss: 0.9902 - acc: 0.750 - ETA: 194s - loss: 0.9785 - acc: 0.750 - ETA: 195s - loss: 0.9852 - acc: 0.733 - ETA: 193s - loss: 0.9768 - acc: 0.728 - ETA: 191s - loss: 0.9597 - acc: 0.731 - ETA: 191s - loss: 0.9583 - acc: 0.733 - ETA: 191s - loss: 1.0033 - acc: 0.715 - ETA: 192s - loss: 0.9886 - acc: 0.713 - ETA: 191s - loss: 0.9425 - acc: 0.729 - ETA: 190s - loss: 0.9690 - acc: 0.723 - ETA: 189s - loss: 0.9907 - acc: 0.710 - ETA: 189s - loss: 0.9889 - acc: 0.710 - ETA: 191s - loss: 0.9866 - acc: 0.709 - ETA: 191s - loss: 1.0001 - acc: 0.694 - ETA: 190s - loss: 0.9985 - acc: 0.691 - ETA: 189s - loss: 0.9701 - acc: 0.700 - ETA: 188s - loss: 1.0024 - acc: 0.697 - ETA: 187s - loss: 0.9955 - acc: 0.702 - ETA: 186s - loss: 0.9912 - acc: 0.706 - ETA: 185s - loss: 0.9944 - acc: 0.702 - ETA: 184s - loss: 0.9954 - acc: 0.700 - ETA: 183s - loss: 0.9938 - acc: 0.700 - ETA: 184s - loss: 0.9810 - acc: 0.700 - ETA: 183s - loss: 0.9762 - acc: 0.701 - ETA: 183s - loss: 0.9889 - acc: 0.703 - ETA: 182s - loss: 1.0004 - acc: 0.698 - ETA: 181s - loss: 1.0124 - acc: 0.691 - ETA: 180s - loss: 1.0124 - 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acc: 0.67 - ETA: 13s - loss: 1.1259 - acc: 0.67 - ETA: 12s - loss: 1.1286 - acc: 0.67 - ETA: 11s - loss: 1.1302 - acc: 0.66 - ETA: 11s - loss: 1.1294 - acc: 0.66 - ETA: 10s - loss: 1.1292 - acc: 0.66 - ETA: 9s - loss: 1.1296 - acc: 0.6696 - ETA: 9s - loss: 1.1291 - acc: 0.669 - ETA: 8s - loss: 1.1279 - acc: 0.670 - ETA: 7s - loss: 1.1275 - acc: 0.670 - ETA: 7s - loss: 1.1302 - acc: 0.669 - ETA: 6s - loss: 1.1293 - acc: 0.669 - ETA: 5s - loss: 1.1297 - acc: 0.669 - ETA: 5s - loss: 1.1298 - acc: 0.668 - ETA: 4s - loss: 1.1305 - acc: 0.668 - ETA: 3s - loss: 1.1300 - acc: 0.668 - ETA: 3s - loss: 1.1280 - acc: 0.668 - ETA: 2s - loss: 1.1294 - acc: 0.668 - ETA: 2s - loss: 1.1283 - acc: 0.668 - ETA: 1s - loss: 1.1277 - acc: 0.668 - ETA: 0s - loss: 1.1276 - acc: 0.6689Epoch 00053: val_loss did not improve 6680/6680 [==============================] - 238s - loss: 1.1292 - acc: 0.6687 - val_loss: 5.4981 - val_acc: 0.0790 Epoch 55/200 4120/6680 [=================>............] - ETA: 212s - loss: 0.6894 - 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acc: 0.754 - ETA: 6s - loss: 0.8347 - acc: 0.753 - ETA: 6s - loss: 0.8361 - acc: 0.753 - ETA: 5s - loss: 0.8356 - acc: 0.753 - ETA: 4s - loss: 0.8355 - acc: 0.753 - ETA: 4s - loss: 0.8368 - acc: 0.753 - ETA: 3s - loss: 0.8355 - acc: 0.753 - ETA: 2s - loss: 0.8342 - acc: 0.753 - ETA: 2s - loss: 0.8345 - acc: 0.753 - ETA: 1s - loss: 0.8344 - acc: 0.753 - ETA: 0s - loss: 0.8343 - acc: 0.7539Epoch 00069: val_loss did not improve 6680/6680 [==============================] - 246s - loss: 0.8347 - acc: 0.7537 - val_loss: 6.2930 - val_acc: 0.0671 Epoch 71/200 4100/6680 [=================>............] - ETA: 216s - loss: 0.2553 - acc: 0.950 - ETA: 212s - loss: 0.4060 - acc: 0.850 - ETA: 207s - loss: 0.5078 - acc: 0.816 - ETA: 214s - loss: 0.5561 - acc: 0.812 - ETA: 236s - loss: 0.8005 - acc: 0.780 - ETA: 241s - loss: 0.8782 - acc: 0.758 - ETA: 240s - loss: 0.8278 - acc: 0.771 - ETA: 236s - loss: 0.7722 - acc: 0.793 - ETA: 231s - loss: 0.7286 - acc: 0.800 - ETA: 227s - loss: 0.7390 - acc: 0.795 - 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ETA: 11s - loss: 0.8144 - acc: 0.76 - ETA: 11s - loss: 0.8159 - acc: 0.75 - ETA: 10s - loss: 0.8159 - acc: 0.75 - ETA: 9s - loss: 0.8157 - acc: 0.7597 - ETA: 9s - loss: 0.8168 - acc: 0.759 - ETA: 8s - loss: 0.8167 - acc: 0.759 - ETA: 7s - loss: 0.8188 - acc: 0.758 - ETA: 7s - loss: 0.8183 - acc: 0.758 - ETA: 6s - loss: 0.8201 - acc: 0.758 - ETA: 5s - loss: 0.8213 - acc: 0.757 - ETA: 4s - loss: 0.8224 - acc: 0.757 - ETA: 4s - loss: 0.8229 - acc: 0.757 - ETA: 3s - loss: 0.8230 - acc: 0.757 - ETA: 2s - loss: 0.8221 - acc: 0.757 - ETA: 2s - loss: 0.8222 - acc: 0.756 - ETA: 1s - loss: 0.8236 - acc: 0.756 - ETA: 0s - loss: 0.8252 - acc: 0.7562Epoch 00070: val_loss did not improve 6680/6680 [==============================] - 251s - loss: 0.8259 - acc: 0.7560 - val_loss: 5.9132 - val_acc: 0.0611 Epoch 72/200 4100/6680 [=================>............] - ETA: 199s - loss: 0.1485 - acc: 1.000 - ETA: 234s - loss: 0.2976 - acc: 0.925 - ETA: 250s - loss: 0.4230 - acc: 0.883 - ETA: 242s - loss: 0.4943 - 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acc: 0.75 - ETA: 16s - loss: 0.8238 - acc: 0.75 - ETA: 15s - loss: 0.8232 - acc: 0.75 - ETA: 14s - loss: 0.8222 - acc: 0.75 - ETA: 14s - loss: 0.8224 - acc: 0.75 - ETA: 13s - loss: 0.8208 - acc: 0.75 - ETA: 12s - loss: 0.8226 - acc: 0.75 - ETA: 11s - loss: 0.8223 - acc: 0.75 - ETA: 11s - loss: 0.8217 - acc: 0.75 - ETA: 10s - loss: 0.8229 - acc: 0.75 - ETA: 9s - loss: 0.8219 - acc: 0.7566 - ETA: 9s - loss: 0.8228 - acc: 0.756 - ETA: 8s - loss: 0.8227 - acc: 0.756 - ETA: 7s - loss: 0.8229 - acc: 0.756 - ETA: 7s - loss: 0.8224 - acc: 0.756 - ETA: 6s - loss: 0.8234 - acc: 0.756 - ETA: 5s - loss: 0.8243 - acc: 0.756 - ETA: 4s - loss: 0.8247 - acc: 0.756 - ETA: 4s - loss: 0.8265 - acc: 0.756 - ETA: 3s - loss: 0.8267 - acc: 0.755 - ETA: 2s - loss: 0.8265 - acc: 0.756 - ETA: 2s - loss: 0.8277 - acc: 0.755 - ETA: 1s - loss: 0.8270 - acc: 0.756 - ETA: 0s - loss: 0.8262 - acc: 0.7563Epoch 00071: val_loss did not improve 6680/6680 [==============================] - 252s - loss: 0.8253 - acc: 0.7567 - val_loss: 6.1729 - val_acc: 0.0707 Epoch 73/200 4100/6680 [=================>............] - ETA: 209s - loss: 0.3284 - acc: 0.950 - ETA: 206s - loss: 0.4827 - acc: 0.875 - ETA: 203s - loss: 0.4779 - acc: 0.866 - ETA: 202s - loss: 0.5867 - acc: 0.850 - ETA: 202s - loss: 0.6045 - acc: 0.820 - ETA: 203s - loss: 0.5872 - acc: 0.825 - ETA: 202s - loss: 0.6030 - acc: 0.821 - ETA: 202s - loss: 0.5866 - acc: 0.806 - ETA: 202s - loss: 0.5927 - acc: 0.800 - ETA: 201s - loss: 0.6149 - acc: 0.790 - ETA: 201s - loss: 0.6785 - acc: 0.781 - ETA: 200s - loss: 0.6573 - acc: 0.787 - ETA: 199s - loss: 0.6582 - acc: 0.788 - ETA: 199s - loss: 0.6620 - acc: 0.792 - ETA: 198s - loss: 0.6866 - acc: 0.790 - ETA: 197s - loss: 0.6719 - acc: 0.793 - ETA: 197s - loss: 0.6489 - acc: 0.802 - ETA: 196s - loss: 0.6313 - acc: 0.805 - ETA: 195s - loss: 0.6393 - acc: 0.807 - ETA: 195s - loss: 0.6740 - acc: 0.802 - ETA: 194s - loss: 0.6695 - acc: 0.804 - ETA: 195s - loss: 0.6619 - acc: 0.806 - ETA: 197s - loss: 0.6625 - 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acc: 0.757 - ETA: 2s - loss: 0.8164 - acc: 0.757 - ETA: 1s - loss: 0.8162 - acc: 0.756 - ETA: 0s - loss: 0.8155 - acc: 0.7571Epoch 00072: val_loss did not improve 6680/6680 [==============================] - 267s - loss: 0.8153 - acc: 0.7572 - val_loss: 5.9180 - val_acc: 0.0683 Epoch 74/200 4100/6680 [=================>............] - ETA: 254s - loss: 0.8617 - acc: 0.800 - ETA: 252s - loss: 1.0276 - acc: 0.725 - ETA: 236s - loss: 0.9682 - acc: 0.766 - ETA: 227s - loss: 0.8188 - acc: 0.787 - ETA: 221s - loss: 0.9022 - acc: 0.730 - ETA: 216s - loss: 0.9206 - acc: 0.733 - ETA: 212s - loss: 0.8585 - acc: 0.750 - ETA: 210s - loss: 0.8709 - acc: 0.743 - ETA: 208s - loss: 0.8224 - acc: 0.755 - ETA: 209s - loss: 0.7838 - acc: 0.770 - ETA: 209s - loss: 0.7730 - acc: 0.781 - ETA: 207s - loss: 0.7391 - acc: 0.791 - ETA: 206s - loss: 0.7593 - acc: 0.784 - ETA: 205s - loss: 0.7848 - acc: 0.782 - ETA: 204s - loss: 0.7676 - acc: 0.786 - ETA: 203s - loss: 0.7476 - acc: 0.793 - ETA: 206s - loss: 0.7592 - 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ETA: 9s - loss: 0.6896 - acc: 0.793 - ETA: 8s - loss: 0.6914 - acc: 0.792 - ETA: 7s - loss: 0.6922 - acc: 0.792 - ETA: 7s - loss: 0.6931 - acc: 0.792 - ETA: 6s - loss: 0.6951 - acc: 0.791 - ETA: 5s - loss: 0.6970 - acc: 0.791 - ETA: 4s - loss: 0.6977 - acc: 0.791 - ETA: 4s - loss: 0.6973 - acc: 0.791 - ETA: 3s - loss: 0.6954 - acc: 0.792 - ETA: 2s - loss: 0.6962 - acc: 0.792 - ETA: 2s - loss: 0.6949 - acc: 0.792 - ETA: 1s - loss: 0.6949 - acc: 0.792 - ETA: 0s - loss: 0.6953 - acc: 0.7923Epoch 00086: val_loss did not improve 6680/6680 [==============================] - 252s - loss: 0.6959 - acc: 0.7924 - val_loss: 6.5982 - val_acc: 0.0587 Epoch 88/200 4100/6680 [=================>............] - ETA: 217s - loss: 1.0203 - acc: 0.750 - ETA: 246s - loss: 1.0214 - acc: 0.775 - ETA: 233s - loss: 1.0122 - acc: 0.733 - ETA: 225s - loss: 0.9125 - acc: 0.762 - ETA: 219s - loss: 0.8642 - acc: 0.760 - ETA: 214s - loss: 0.7912 - acc: 0.775 - ETA: 214s - loss: 0.7198 - acc: 0.785 - ETA: 213s - loss: 0.6579 - acc: 0.800 - ETA: 213s - loss: 0.6529 - acc: 0.794 - ETA: 212s - loss: 0.6677 - acc: 0.800 - ETA: 211s - loss: 0.6414 - acc: 0.809 - ETA: 216s - loss: 0.6042 - acc: 0.820 - ETA: 216s - loss: 0.6140 - acc: 0.811 - ETA: 217s - loss: 0.5943 - acc: 0.814 - ETA: 216s - loss: 0.5955 - acc: 0.806 - ETA: 219s - loss: 0.5826 - acc: 0.809 - ETA: 225s - loss: 0.5844 - acc: 0.808 - ETA: 225s - loss: 0.5915 - acc: 0.805 - ETA: 226s - loss: 0.6181 - acc: 0.800 - ETA: 224s - loss: 0.6131 - acc: 0.805 - ETA: 223s - loss: 0.5954 - acc: 0.809 - ETA: 222s - loss: 0.5901 - acc: 0.811 - ETA: 220s - loss: 0.5882 - acc: 0.813 - ETA: 218s - loss: 0.5866 - acc: 0.810 - ETA: 218s - loss: 0.5782 - acc: 0.810 - ETA: 219s - loss: 0.5705 - acc: 0.815 - ETA: 217s - loss: 0.5734 - acc: 0.816 - ETA: 218s - loss: 0.5616 - acc: 0.821 - ETA: 219s - loss: 0.5590 - acc: 0.822 - ETA: 218s - loss: 0.5613 - acc: 0.821 - ETA: 216s - loss: 0.5552 - acc: 0.824 - ETA: 216s - loss: 0.5641 - acc: 0.823 - ETA: 214s - loss: 0.5610 - 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acc: 0.803 - ETA: 104s - loss: 0.6437 - acc: 0.803 - ETA: 104s - loss: 0.6443 - acc: 0.803 - ETA: 103s - loss: 0.6463 - acc: 0.803 - ETA: 102s - loss: 0.6473 - acc: 0.802 - ETA: 102s - loss: 0.6467 - acc: 0.802 - ETA: 101s - loss: 0.6504 - acc: 0.801 - ETA: 100s - loss: 0.6488 - acc: 0.801 - ETA: 100s - loss: 0.6484 - acc: 0.801 - ETA: 99s - loss: 0.6482 - acc: 0.801 - ETA: 98s - loss: 0.6482 - acc: 0.80 - ETA: 97s - loss: 0.6475 - acc: 0.80 - ETA: 97s - loss: 0.6472 - acc: 0.80 - ETA: 96s - loss: 0.6487 - acc: 0.80 - ETA: 95s - loss: 0.6499 - acc: 0.80 - ETA: 95s - loss: 0.6491 - acc: 0.80 - ETA: 94s - loss: 0.6525 - acc: 0.80 - ETA: 93s - loss: 0.6524 - acc: 0.80 - ETA: 93s - loss: 0.6517 - acc: 0.80 - ETA: 92s - loss: 0.6547 - acc: 0.80 - ETA: 91s - loss: 0.6566 - acc: 0.79 - ETA: 90s - loss: 0.6556 - acc: 0.80 - ETA: 90s - loss: 0.6567 - acc: 0.79936660/6680 [============================>.] - ETA: 89s - loss: 0.6549 - acc: 0.80 - ETA: 88s - loss: 0.6548 - acc: 0.80 - ETA: 88s - loss: 0.6537 - acc: 0.80 - ETA: 87s - loss: 0.6555 - acc: 0.80 - ETA: 86s - loss: 0.6548 - acc: 0.80 - ETA: 86s - loss: 0.6545 - acc: 0.80 - ETA: 85s - loss: 0.6532 - acc: 0.80 - ETA: 84s - loss: 0.6525 - acc: 0.80 - ETA: 84s - loss: 0.6541 - acc: 0.80 - ETA: 83s - loss: 0.6530 - acc: 0.80 - ETA: 82s - loss: 0.6546 - acc: 0.80 - ETA: 81s - loss: 0.6533 - acc: 0.80 - ETA: 81s - loss: 0.6529 - acc: 0.80 - ETA: 80s - loss: 0.6529 - acc: 0.80 - ETA: 79s - loss: 0.6541 - acc: 0.80 - ETA: 79s - loss: 0.6539 - acc: 0.80 - ETA: 78s - loss: 0.6540 - acc: 0.79 - ETA: 77s - loss: 0.6563 - acc: 0.79 - ETA: 76s - loss: 0.6570 - acc: 0.79 - ETA: 76s - loss: 0.6580 - acc: 0.79 - ETA: 75s - loss: 0.6588 - acc: 0.79 - ETA: 74s - loss: 0.6594 - acc: 0.80 - ETA: 73s - loss: 0.6594 - acc: 0.80 - ETA: 73s - loss: 0.6581 - acc: 0.80 - ETA: 72s - loss: 0.6572 - acc: 0.80 - ETA: 72s - loss: 0.6572 - acc: 0.80 - ETA: 71s - loss: 0.6563 - acc: 0.80 - ETA: 70s - loss: 0.6552 - acc: 0.80 - ETA: 70s - loss: 0.6537 - acc: 0.80 - 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acc: 0.79 - ETA: 13s - loss: 0.6627 - acc: 0.79 - ETA: 12s - loss: 0.6630 - acc: 0.79 - ETA: 12s - loss: 0.6630 - acc: 0.79 - ETA: 11s - loss: 0.6623 - acc: 0.79 - ETA: 10s - loss: 0.6627 - acc: 0.79 - ETA: 9s - loss: 0.6625 - acc: 0.7980 - ETA: 9s - loss: 0.6633 - acc: 0.797 - ETA: 8s - loss: 0.6624 - acc: 0.798 - ETA: 7s - loss: 0.6638 - acc: 0.797 - ETA: 7s - loss: 0.6664 - acc: 0.797 - ETA: 6s - loss: 0.6657 - acc: 0.797 - ETA: 5s - loss: 0.6659 - acc: 0.797 - ETA: 4s - loss: 0.6680 - acc: 0.796 - ETA: 4s - loss: 0.6681 - acc: 0.797 - ETA: 3s - loss: 0.6699 - acc: 0.796 - ETA: 2s - loss: 0.6712 - acc: 0.796 - ETA: 2s - loss: 0.6724 - acc: 0.795 - ETA: 1s - loss: 0.6723 - acc: 0.795 - ETA: 0s - loss: 0.6727 - acc: 0.7953Epoch 00087: val_loss did not improve 6680/6680 [==============================] - 249s - loss: 0.6733 - acc: 0.7951 - val_loss: 6.4800 - val_acc: 0.0563 Epoch 89/200 4100/6680 [=================>............] - ETA: 205s - loss: 1.1922 - acc: 0.700 - ETA: 201s - loss: 0.9003 - acc: 0.725 - ETA: 200s - loss: 1.0613 - acc: 0.733 - ETA: 200s - loss: 1.1242 - acc: 0.712 - ETA: 199s - loss: 1.0363 - acc: 0.730 - ETA: 198s - loss: 1.0052 - acc: 0.733 - ETA: 197s - loss: 0.9417 - acc: 0.742 - ETA: 196s - loss: 0.8812 - acc: 0.762 - ETA: 195s - loss: 0.8083 - acc: 0.783 - ETA: 197s - loss: 0.7949 - acc: 0.775 - ETA: 197s - loss: 0.7697 - acc: 0.781 - ETA: 197s - loss: 0.7318 - acc: 0.795 - ETA: 197s - loss: 0.7110 - acc: 0.800 - ETA: 200s - loss: 0.7075 - acc: 0.796 - ETA: 204s - loss: 0.6879 - acc: 0.803 - ETA: 203s - loss: 0.6704 - acc: 0.809 - ETA: 202s - loss: 0.6700 - acc: 0.805 - ETA: 201s - loss: 0.6623 - acc: 0.808 - ETA: 201s - loss: 0.6722 - acc: 0.805 - ETA: 200s - loss: 0.6802 - acc: 0.810 - ETA: 199s - loss: 0.6799 - acc: 0.811 - ETA: 198s - loss: 0.6787 - acc: 0.809 - ETA: 200s - loss: 0.6749 - acc: 0.806 - ETA: 200s - loss: 0.6631 - acc: 0.810 - ETA: 199s - loss: 0.6733 - acc: 0.808 - ETA: 199s - loss: 0.6821 - acc: 0.801 - ETA: 197s - loss: 0.6805 - 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acc: 0.80 - ETA: 89s - loss: 0.6522 - acc: 0.80 - ETA: 89s - loss: 0.6520 - acc: 0.80716660/6680 [============================>.] - ETA: 88s - loss: 0.6507 - acc: 0.80 - ETA: 87s - loss: 0.6510 - acc: 0.80 - ETA: 86s - loss: 0.6534 - acc: 0.80 - ETA: 86s - loss: 0.6520 - acc: 0.80 - ETA: 85s - loss: 0.6518 - acc: 0.80 - ETA: 84s - loss: 0.6505 - acc: 0.80 - ETA: 84s - loss: 0.6508 - acc: 0.80 - ETA: 83s - loss: 0.6511 - acc: 0.80 - ETA: 82s - loss: 0.6501 - acc: 0.80 - ETA: 81s - loss: 0.6481 - acc: 0.80 - ETA: 81s - loss: 0.6465 - acc: 0.80 - ETA: 80s - loss: 0.6459 - acc: 0.80 - ETA: 79s - loss: 0.6476 - acc: 0.80 - ETA: 79s - loss: 0.6480 - acc: 0.80 - ETA: 78s - loss: 0.6466 - acc: 0.80 - ETA: 77s - loss: 0.6467 - acc: 0.80 - ETA: 77s - loss: 0.6475 - acc: 0.80 - ETA: 76s - loss: 0.6477 - acc: 0.80 - ETA: 75s - loss: 0.6501 - acc: 0.80 - ETA: 75s - loss: 0.6515 - acc: 0.80 - ETA: 74s - loss: 0.6533 - acc: 0.80 - ETA: 73s - loss: 0.6519 - acc: 0.80 - ETA: 73s - loss: 0.6509 - acc: 0.80 - 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acc: 0.80 - ETA: 17s - loss: 0.6603 - acc: 0.80 - ETA: 16s - loss: 0.6593 - acc: 0.80 - ETA: 15s - loss: 0.6585 - acc: 0.80 - ETA: 15s - loss: 0.6572 - acc: 0.80 - ETA: 14s - loss: 0.6578 - acc: 0.80 - ETA: 13s - loss: 0.6588 - acc: 0.80 - ETA: 13s - loss: 0.6620 - acc: 0.80 - ETA: 12s - loss: 0.6613 - acc: 0.80 - ETA: 11s - loss: 0.6621 - acc: 0.80 - ETA: 11s - loss: 0.6622 - acc: 0.80 - ETA: 10s - loss: 0.6633 - acc: 0.80 - ETA: 9s - loss: 0.6637 - acc: 0.8017 - ETA: 9s - loss: 0.6636 - acc: 0.801 - ETA: 8s - loss: 0.6635 - acc: 0.801 - ETA: 7s - loss: 0.6634 - acc: 0.801 - ETA: 6s - loss: 0.6635 - acc: 0.801 - ETA: 6s - loss: 0.6639 - acc: 0.801 - ETA: 5s - loss: 0.6638 - acc: 0.801 - ETA: 4s - loss: 0.6627 - acc: 0.801 - ETA: 4s - loss: 0.6642 - acc: 0.801 - ETA: 3s - loss: 0.6654 - acc: 0.801 - ETA: 2s - loss: 0.6663 - acc: 0.800 - ETA: 2s - loss: 0.6654 - acc: 0.801 - ETA: 1s - loss: 0.6646 - acc: 0.801 - ETA: 0s - loss: 0.6636 - acc: 0.8015Epoch 00088: val_loss did not improve 6680/6680 [==============================] - 249s - loss: 0.6644 - acc: 0.8013 - val_loss: 6.5382 - val_acc: 0.0671 Epoch 90/200 4100/6680 [=================>............] - ETA: 220s - loss: 0.5717 - acc: 0.850 - ETA: 218s - loss: 0.7784 - acc: 0.775 - ETA: 218s - loss: 0.8066 - acc: 0.766 - ETA: 216s - loss: 0.7435 - acc: 0.787 - ETA: 212s - loss: 0.7590 - acc: 0.770 - ETA: 211s - loss: 0.7467 - acc: 0.791 - ETA: 209s - loss: 0.7035 - acc: 0.807 - ETA: 207s - loss: 0.6817 - acc: 0.806 - ETA: 205s - loss: 0.6640 - acc: 0.805 - ETA: 210s - loss: 0.6616 - acc: 0.810 - ETA: 215s - loss: 0.6686 - acc: 0.809 - ETA: 214s - loss: 0.6553 - acc: 0.816 - ETA: 212s - loss: 0.6318 - acc: 0.826 - ETA: 210s - loss: 0.6556 - acc: 0.825 - ETA: 209s - loss: 0.6587 - acc: 0.823 - ETA: 208s - loss: 0.6360 - acc: 0.834 - ETA: 207s - loss: 0.6182 - acc: 0.838 - ETA: 206s - loss: 0.6238 - acc: 0.836 - ETA: 205s - loss: 0.5998 - acc: 0.842 - ETA: 204s - loss: 0.5912 - acc: 0.840 - ETA: 203s - loss: 0.5831 - acc: 0.842 - ETA: 201s - loss: 0.5938 - acc: 0.843 - ETA: 200s - loss: 0.6008 - acc: 0.837 - ETA: 200s - loss: 0.6210 - acc: 0.829 - ETA: 198s - loss: 0.6322 - acc: 0.822 - ETA: 198s - loss: 0.6413 - acc: 0.819 - ETA: 199s - loss: 0.6291 - acc: 0.824 - ETA: 200s - loss: 0.6345 - acc: 0.823 - ETA: 199s - loss: 0.6442 - acc: 0.820 - ETA: 198s - loss: 0.6512 - acc: 0.820 - ETA: 197s - loss: 0.6516 - acc: 0.819 - ETA: 196s - loss: 0.6551 - acc: 0.820 - ETA: 195s - loss: 0.6545 - acc: 0.821 - ETA: 195s - loss: 0.6632 - acc: 0.817 - ETA: 194s - loss: 0.6759 - acc: 0.815 - ETA: 194s - loss: 0.6834 - acc: 0.812 - ETA: 193s - loss: 0.6894 - acc: 0.812 - ETA: 192s - loss: 0.6851 - acc: 0.813 - ETA: 191s - loss: 0.6962 - acc: 0.810 - ETA: 191s - loss: 0.7020 - acc: 0.808 - ETA: 191s - loss: 0.6937 - acc: 0.809 - ETA: 191s - loss: 0.7009 - acc: 0.807 - ETA: 191s - loss: 0.6965 - acc: 0.807 - ETA: 192s - loss: 0.6919 - acc: 0.809 - ETA: 192s - loss: 0.6920 - acc: 0.808 - ETA: 191s - loss: 0.7075 - acc: 0.805 - ETA: 190s - loss: 0.7073 - acc: 0.804 - ETA: 189s - loss: 0.7065 - acc: 0.804 - ETA: 189s - loss: 0.6967 - acc: 0.806 - ETA: 188s - loss: 0.7056 - acc: 0.804 - ETA: 187s - loss: 0.7045 - acc: 0.803 - ETA: 186s - loss: 0.7045 - acc: 0.803 - ETA: 185s - loss: 0.7014 - acc: 0.801 - ETA: 184s - loss: 0.6919 - acc: 0.805 - ETA: 183s - loss: 0.6907 - acc: 0.802 - ETA: 183s - loss: 0.6845 - acc: 0.804 - ETA: 182s - loss: 0.6792 - acc: 0.807 - ETA: 183s - loss: 0.6780 - acc: 0.805 - ETA: 184s - loss: 0.6756 - acc: 0.804 - ETA: 185s - loss: 0.6699 - acc: 0.806 - ETA: 184s - loss: 0.6638 - acc: 0.808 - ETA: 183s - loss: 0.6626 - acc: 0.809 - ETA: 182s - loss: 0.6591 - acc: 0.809 - ETA: 181s - loss: 0.6630 - acc: 0.810 - ETA: 181s - loss: 0.6626 - acc: 0.810 - ETA: 180s - loss: 0.6586 - acc: 0.811 - ETA: 179s - loss: 0.6540 - acc: 0.812 - ETA: 178s - loss: 0.6493 - acc: 0.814 - ETA: 177s - loss: 0.6508 - acc: 0.812 - ETA: 176s - loss: 0.6499 - acc: 0.812 - ETA: 176s - loss: 0.6496 - acc: 0.813 - ETA: 175s - loss: 0.6507 - acc: 0.812 - 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ETA: 156s - loss: 0.6764 - acc: 0.804 - ETA: 156s - loss: 0.6713 - acc: 0.805 - ETA: 155s - loss: 0.6709 - acc: 0.805 - ETA: 154s - loss: 0.6682 - acc: 0.805 - ETA: 153s - loss: 0.6646 - acc: 0.805 - ETA: 153s - loss: 0.6640 - acc: 0.805 - ETA: 152s - loss: 0.6640 - acc: 0.806 - ETA: 152s - loss: 0.6685 - acc: 0.804 - ETA: 151s - loss: 0.6739 - acc: 0.802 - ETA: 150s - loss: 0.6719 - acc: 0.802 - ETA: 149s - loss: 0.6721 - acc: 0.802 - ETA: 149s - loss: 0.6718 - acc: 0.802 - ETA: 148s - loss: 0.6713 - acc: 0.802 - ETA: 147s - loss: 0.6708 - acc: 0.801 - ETA: 146s - loss: 0.6689 - acc: 0.801 - ETA: 146s - loss: 0.6685 - acc: 0.801 - ETA: 145s - loss: 0.6673 - acc: 0.801 - ETA: 145s - loss: 0.6696 - acc: 0.801 - ETA: 144s - loss: 0.6691 - acc: 0.801 - ETA: 143s - loss: 0.6723 - acc: 0.801 - ETA: 143s - loss: 0.6754 - acc: 0.800 - ETA: 142s - loss: 0.6760 - acc: 0.800 - ETA: 141s - loss: 0.6780 - acc: 0.798 - ETA: 141s - loss: 0.6787 - acc: 0.798 - ETA: 140s - loss: 0.6766 - acc: 0.799 - 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acc: 0.79 - ETA: 21s - loss: 0.6775 - acc: 0.79 - ETA: 20s - loss: 0.6798 - acc: 0.79 - ETA: 19s - loss: 0.6802 - acc: 0.79 - ETA: 19s - loss: 0.6798 - acc: 0.79 - ETA: 18s - loss: 0.6820 - acc: 0.79 - ETA: 17s - loss: 0.6828 - acc: 0.79 - ETA: 17s - loss: 0.6847 - acc: 0.79 - ETA: 16s - loss: 0.6841 - acc: 0.79 - ETA: 15s - loss: 0.6846 - acc: 0.79 - ETA: 15s - loss: 0.6838 - acc: 0.79 - ETA: 14s - loss: 0.6826 - acc: 0.79 - ETA: 13s - loss: 0.6810 - acc: 0.79 - ETA: 12s - loss: 0.6829 - acc: 0.79 - ETA: 12s - loss: 0.6824 - acc: 0.79 - ETA: 11s - loss: 0.6841 - acc: 0.79 - ETA: 10s - loss: 0.6851 - acc: 0.79 - ETA: 10s - loss: 0.6858 - acc: 0.79 - ETA: 9s - loss: 0.6878 - acc: 0.7964 - ETA: 8s - loss: 0.6883 - acc: 0.796 - ETA: 7s - loss: 0.6897 - acc: 0.796 - ETA: 7s - loss: 0.6893 - acc: 0.796 - ETA: 6s - loss: 0.6893 - acc: 0.796 - ETA: 5s - loss: 0.6887 - acc: 0.796 - ETA: 4s - loss: 0.6878 - acc: 0.796 - ETA: 4s - loss: 0.6877 - acc: 0.796 - ETA: 3s - loss: 0.6865 - acc: 0.796 - 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acc: 0.795 - ETA: 7s - loss: 0.6660 - acc: 0.795 - ETA: 6s - loss: 0.6662 - acc: 0.795 - ETA: 5s - loss: 0.6669 - acc: 0.795 - ETA: 5s - loss: 0.6663 - acc: 0.795 - ETA: 4s - loss: 0.6665 - acc: 0.795 - ETA: 3s - loss: 0.6661 - acc: 0.796 - ETA: 2s - loss: 0.6675 - acc: 0.795 - ETA: 2s - loss: 0.6665 - acc: 0.796 - ETA: 1s - loss: 0.6666 - acc: 0.795 - ETA: 0s - loss: 0.6666 - acc: 0.7958Epoch 00090: val_loss did not improve 6680/6680 [==============================] - 256s - loss: 0.6670 - acc: 0.7958 - val_loss: 6.7926 - val_acc: 0.0611 Epoch 92/200 4100/6680 [=================>............] - ETA: 213s - loss: 0.6453 - acc: 0.850 - ETA: 208s - loss: 0.5092 - acc: 0.850 - ETA: 219s - loss: 0.4962 - acc: 0.866 - ETA: 233s - loss: 0.4400 - acc: 0.887 - ETA: 225s - loss: 0.4161 - acc: 0.890 - ETA: 221s - loss: 0.4108 - acc: 0.891 - ETA: 217s - loss: 0.3919 - acc: 0.900 - ETA: 217s - loss: 0.4205 - acc: 0.875 - ETA: 214s - loss: 0.4526 - acc: 0.866 - ETA: 211s - loss: 0.5102 - acc: 0.855 - 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acc: 0.80 - ETA: 16s - loss: 0.6464 - acc: 0.80 - ETA: 15s - loss: 0.6464 - acc: 0.80 - ETA: 15s - loss: 0.6471 - acc: 0.80 - ETA: 14s - loss: 0.6466 - acc: 0.80 - ETA: 13s - loss: 0.6490 - acc: 0.80 - ETA: 12s - loss: 0.6491 - acc: 0.80 - ETA: 12s - loss: 0.6499 - acc: 0.80 - ETA: 11s - loss: 0.6506 - acc: 0.80 - ETA: 10s - loss: 0.6512 - acc: 0.80 - ETA: 10s - loss: 0.6515 - acc: 0.80 - ETA: 9s - loss: 0.6500 - acc: 0.8037 - ETA: 8s - loss: 0.6494 - acc: 0.803 - ETA: 7s - loss: 0.6490 - acc: 0.803 - ETA: 7s - loss: 0.6478 - acc: 0.804 - ETA: 6s - loss: 0.6496 - acc: 0.804 - ETA: 5s - loss: 0.6509 - acc: 0.804 - ETA: 5s - loss: 0.6512 - acc: 0.804 - ETA: 4s - loss: 0.6515 - acc: 0.803 - ETA: 3s - loss: 0.6523 - acc: 0.803 - ETA: 2s - loss: 0.6520 - acc: 0.803 - ETA: 2s - loss: 0.6522 - acc: 0.803 - ETA: 1s - loss: 0.6518 - acc: 0.802 - ETA: 0s - loss: 0.6512 - acc: 0.8032Epoch 00092: val_loss did not improve 6680/6680 [==============================] - 257s - loss: 0.6512 - acc: 0.8031 - 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acc: 0.82 - ETA: 15s - loss: 0.5924 - acc: 0.82 - ETA: 15s - loss: 0.5922 - acc: 0.82 - ETA: 14s - loss: 0.5920 - acc: 0.82 - ETA: 13s - loss: 0.5916 - acc: 0.82 - ETA: 13s - loss: 0.5917 - acc: 0.82 - ETA: 12s - loss: 0.5908 - acc: 0.82 - ETA: 11s - loss: 0.5920 - acc: 0.82 - ETA: 11s - loss: 0.5905 - acc: 0.82 - ETA: 10s - loss: 0.5909 - acc: 0.82 - ETA: 9s - loss: 0.5903 - acc: 0.8248 - ETA: 9s - loss: 0.5896 - acc: 0.824 - ETA: 8s - loss: 0.5904 - acc: 0.824 - ETA: 7s - loss: 0.5896 - acc: 0.824 - ETA: 7s - loss: 0.5896 - acc: 0.824 - ETA: 6s - loss: 0.5893 - acc: 0.824 - ETA: 5s - loss: 0.5900 - acc: 0.824 - ETA: 5s - loss: 0.5924 - acc: 0.823 - ETA: 4s - loss: 0.5928 - acc: 0.823 - ETA: 3s - loss: 0.5941 - acc: 0.823 - ETA: 3s - loss: 0.5945 - acc: 0.823 - ETA: 2s - loss: 0.5945 - acc: 0.823 - ETA: 1s - loss: 0.5930 - acc: 0.823 - ETA: 1s - loss: 0.5923 - acc: 0.823 - ETA: 0s - loss: 0.5938 - acc: 0.8237Epoch 00105: val_loss did not improve 6680/6680 [==============================] - 239s - loss: 0.5924 - acc: 0.8243 - val_loss: 6.8207 - val_acc: 0.0743 Epoch 107/200 4120/6680 [=================>............] - ETA: 232s - loss: 0.8516 - acc: 0.700 - ETA: 209s - loss: 0.6113 - acc: 0.800 - ETA: 203s - loss: 0.5110 - acc: 0.833 - ETA: 198s - loss: 0.4750 - acc: 0.850 - ETA: 195s - loss: 0.5376 - acc: 0.830 - ETA: 192s - loss: 0.4787 - acc: 0.850 - ETA: 195s - loss: 0.5130 - acc: 0.828 - ETA: 195s - loss: 0.4754 - acc: 0.843 - ETA: 208s - loss: 0.5882 - acc: 0.838 - ETA: 209s - loss: 0.5707 - acc: 0.835 - ETA: 207s - loss: 0.5369 - acc: 0.840 - ETA: 205s - loss: 0.5563 - acc: 0.837 - ETA: 204s - loss: 0.5344 - acc: 0.838 - ETA: 202s - loss: 0.5320 - acc: 0.835 - ETA: 201s - loss: 0.5388 - acc: 0.840 - ETA: 199s - loss: 0.5412 - acc: 0.834 - ETA: 198s - loss: 0.5194 - acc: 0.841 - ETA: 197s - loss: 0.5252 - acc: 0.841 - ETA: 196s - loss: 0.5638 - acc: 0.834 - ETA: 194s - loss: 0.5497 - acc: 0.837 - ETA: 193s - loss: 0.5555 - acc: 0.835 - ETA: 192s - loss: 0.5484 - acc: 0.838 - 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acc: 0.816 - ETA: 1s - loss: 0.6116 - acc: 0.816 - ETA: 1s - loss: 0.6114 - acc: 0.816 - ETA: 0s - loss: 0.6103 - acc: 0.8171Epoch 00106: val_loss did not improve 6680/6680 [==============================] - 236s - loss: 0.6107 - acc: 0.8172 - val_loss: 6.9780 - val_acc: 0.0551 Epoch 108/200 4100/6680 [=================>............] - ETA: 232s - loss: 0.7705 - acc: 0.750 - ETA: 210s - loss: 0.5104 - acc: 0.825 - ETA: 225s - loss: 0.5403 - acc: 0.833 - ETA: 217s - loss: 0.4809 - acc: 0.850 - ETA: 213s - loss: 0.4570 - acc: 0.860 - ETA: 208s - loss: 0.4704 - acc: 0.841 - ETA: 205s - loss: 0.4583 - acc: 0.850 - ETA: 202s - loss: 0.4493 - acc: 0.850 - ETA: 201s - loss: 0.4292 - acc: 0.861 - ETA: 200s - loss: 0.4290 - acc: 0.865 - ETA: 200s - loss: 0.4436 - acc: 0.863 - ETA: 205s - loss: 0.4377 - acc: 0.866 - ETA: 205s - loss: 0.4371 - acc: 0.865 - ETA: 205s - loss: 0.4169 - acc: 0.871 - ETA: 214s - loss: 0.4155 - acc: 0.873 - ETA: 212s - loss: 0.4349 - acc: 0.871 - ETA: 212s - loss: 0.4374 - 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acc: 0.822 - ETA: 6s - loss: 0.6088 - acc: 0.822 - ETA: 5s - loss: 0.6084 - acc: 0.822 - ETA: 4s - loss: 0.6085 - acc: 0.822 - ETA: 4s - loss: 0.6097 - acc: 0.822 - ETA: 3s - loss: 0.6096 - acc: 0.821 - ETA: 2s - loss: 0.6092 - acc: 0.821 - ETA: 2s - loss: 0.6093 - acc: 0.821 - ETA: 1s - loss: 0.6091 - acc: 0.821 - ETA: 0s - loss: 0.6085 - acc: 0.8219Epoch 00107: val_loss did not improve 6680/6680 [==============================] - 252s - loss: 0.6082 - acc: 0.8219 - val_loss: 6.7782 - val_acc: 0.0647 Epoch 109/200 4080/6680 [=================>............] - ETA: 319s - loss: 0.3374 - acc: 0.900 - ETA: 257s - loss: 0.4334 - acc: 0.850 - ETA: 240s - loss: 0.3964 - acc: 0.850 - ETA: 243s - loss: 0.4239 - acc: 0.850 - ETA: 237s - loss: 0.4924 - acc: 0.840 - ETA: 233s - loss: 0.4326 - acc: 0.866 - ETA: 235s - loss: 0.4642 - acc: 0.857 - ETA: 236s - loss: 0.5141 - acc: 0.856 - ETA: 241s - loss: 0.5071 - acc: 0.861 - ETA: 258s - loss: 0.4890 - acc: 0.870 - ETA: 269s - loss: 0.4600 - acc: 0.877 - 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acc: 0.82 - ETA: 14s - loss: 0.5703 - acc: 0.82 - ETA: 13s - loss: 0.5707 - acc: 0.82 - ETA: 12s - loss: 0.5701 - acc: 0.82 - ETA: 11s - loss: 0.5692 - acc: 0.82 - ETA: 11s - loss: 0.5682 - acc: 0.82 - ETA: 10s - loss: 0.5685 - acc: 0.82 - ETA: 9s - loss: 0.5679 - acc: 0.8299 - ETA: 8s - loss: 0.5680 - acc: 0.830 - ETA: 7s - loss: 0.5685 - acc: 0.829 - ETA: 6s - loss: 0.5690 - acc: 0.829 - ETA: 5s - loss: 0.5681 - acc: 0.830 - ETA: 5s - loss: 0.5689 - acc: 0.829 - ETA: 4s - loss: 0.5685 - acc: 0.829 - ETA: 3s - loss: 0.5692 - acc: 0.829 - ETA: 2s - loss: 0.5685 - acc: 0.829 - ETA: 1s - loss: 0.5683 - acc: 0.829 - ETA: 0s - loss: 0.5685 - acc: 0.8290Epoch 00108: val_loss did not improve 6680/6680 [==============================] - 303s - loss: 0.5709 - acc: 0.8283 - val_loss: 6.4927 - val_acc: 0.0587 Epoch 110/200 4100/6680 [=================>............] - ETA: 234s - loss: 0.8312 - acc: 0.700 - ETA: 272s - loss: 0.6016 - acc: 0.800 - ETA: 253s - loss: 0.6038 - acc: 0.783 - ETA: 245s - 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acc: 0.82 - ETA: 13s - loss: 0.5640 - acc: 0.82 - ETA: 12s - loss: 0.5632 - acc: 0.82 - ETA: 11s - loss: 0.5650 - acc: 0.82 - ETA: 10s - loss: 0.5664 - acc: 0.82 - ETA: 9s - loss: 0.5660 - acc: 0.8288 - ETA: 8s - loss: 0.5664 - acc: 0.828 - ETA: 7s - loss: 0.5677 - acc: 0.828 - ETA: 6s - loss: 0.5675 - acc: 0.828 - ETA: 5s - loss: 0.5665 - acc: 0.828 - ETA: 4s - loss: 0.5653 - acc: 0.829 - ETA: 3s - loss: 0.5647 - acc: 0.829 - ETA: 2s - loss: 0.5640 - acc: 0.829 - ETA: 1s - loss: 0.5645 - acc: 0.8293Epoch 00127: val_loss did not improve 6680/6680 [==============================] - 368s - loss: 0.5637 - acc: 0.8295 - val_loss: 7.0356 - val_acc: 0.0671 Epoch 129/200 4080/6680 [=================>............] - ETA: 288s - loss: 0.5551 - acc: 0.750 - ETA: 288s - loss: 0.4822 - acc: 0.775 - ETA: 285s - loss: 0.4636 - acc: 0.816 - ETA: 282s - loss: 0.5432 - acc: 0.812 - ETA: 287s - loss: 0.5221 - acc: 0.820 - ETA: 287s - loss: 0.6438 - acc: 0.800 - ETA: 291s - loss: 0.5848 - acc: 0.821 - ETA: 290s - 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ETA: 24s - loss: 0.5597 - acc: 0.83 - ETA: 23s - loss: 0.5603 - acc: 0.83 - ETA: 22s - loss: 0.5597 - acc: 0.83 - ETA: 21s - loss: 0.5587 - acc: 0.83 - ETA: 20s - loss: 0.5582 - acc: 0.83 - ETA: 19s - loss: 0.5593 - acc: 0.83 - ETA: 18s - loss: 0.5617 - acc: 0.83 - ETA: 17s - loss: 0.5610 - acc: 0.83 - ETA: 15s - loss: 0.5611 - acc: 0.83 - ETA: 14s - loss: 0.5614 - acc: 0.83 - ETA: 13s - loss: 0.5607 - acc: 0.83 - ETA: 12s - loss: 0.5612 - acc: 0.83 - ETA: 11s - loss: 0.5609 - acc: 0.83 - ETA: 10s - loss: 0.5607 - acc: 0.83 - ETA: 9s - loss: 0.5599 - acc: 0.8357 - ETA: 8s - loss: 0.5590 - acc: 0.836 - ETA: 7s - loss: 0.5594 - acc: 0.836 - ETA: 6s - loss: 0.5591 - acc: 0.836 - ETA: 5s - loss: 0.5588 - acc: 0.836 - ETA: 4s - loss: 0.5577 - acc: 0.836 - ETA: 3s - loss: 0.5600 - acc: 0.836 - ETA: 2s - loss: 0.5604 - acc: 0.836 - ETA: 1s - loss: 0.5602 - acc: 0.8363Epoch 00128: val_loss did not improve 6680/6680 [==============================] - 373s - loss: 0.5603 - acc: 0.8362 - val_loss: 7.4974 - 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ETA: 12s - loss: 0.5343 - acc: 0.84 - ETA: 12s - loss: 0.5331 - acc: 0.84 - ETA: 11s - loss: 0.5341 - acc: 0.84 - ETA: 10s - loss: 0.5335 - acc: 0.84 - ETA: 10s - loss: 0.5345 - acc: 0.84 - ETA: 9s - loss: 0.5348 - acc: 0.8431 - ETA: 8s - loss: 0.5359 - acc: 0.842 - ETA: 8s - loss: 0.5358 - acc: 0.843 - ETA: 7s - loss: 0.5359 - acc: 0.842 - ETA: 6s - loss: 0.5351 - acc: 0.843 - ETA: 6s - loss: 0.5350 - acc: 0.843 - ETA: 5s - loss: 0.5338 - acc: 0.843 - ETA: 5s - loss: 0.5344 - acc: 0.843 - ETA: 4s - loss: 0.5352 - acc: 0.843 - ETA: 3s - loss: 0.5345 - acc: 0.843 - ETA: 3s - loss: 0.5348 - acc: 0.842 - ETA: 2s - loss: 0.5346 - acc: 0.842 - ETA: 1s - loss: 0.5349 - acc: 0.842 - ETA: 1s - loss: 0.5340 - acc: 0.842 - ETA: 0s - loss: 0.5339 - acc: 0.8428Epoch 00144: val_loss did not improve 6680/6680 [==============================] - 221s - loss: 0.5339 - acc: 0.8427 - val_loss: 7.3897 - val_acc: 0.0611 Epoch 146/200 4120/6680 [=================>............] - ETA: 202s - loss: 0.8987 - acc: 0.800 - 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acc: 0.83 - ETA: 15s - loss: 0.5488 - acc: 0.83 - ETA: 14s - loss: 0.5488 - acc: 0.83 - ETA: 14s - loss: 0.5485 - acc: 0.83 - ETA: 13s - loss: 0.5478 - acc: 0.83 - ETA: 13s - loss: 0.5498 - acc: 0.83 - ETA: 12s - loss: 0.5506 - acc: 0.83 - ETA: 11s - loss: 0.5495 - acc: 0.83 - ETA: 11s - loss: 0.5500 - acc: 0.83 - ETA: 10s - loss: 0.5491 - acc: 0.83 - ETA: 9s - loss: 0.5475 - acc: 0.8373 - ETA: 9s - loss: 0.5472 - acc: 0.837 - ETA: 8s - loss: 0.5502 - acc: 0.836 - ETA: 8s - loss: 0.5536 - acc: 0.836 - ETA: 7s - loss: 0.5534 - acc: 0.836 - ETA: 6s - loss: 0.5538 - acc: 0.836 - ETA: 6s - loss: 0.5553 - acc: 0.835 - ETA: 5s - loss: 0.5558 - acc: 0.835 - ETA: 4s - loss: 0.5554 - acc: 0.836 - ETA: 4s - loss: 0.5586 - acc: 0.835 - ETA: 3s - loss: 0.5601 - acc: 0.835 - ETA: 3s - loss: 0.5596 - acc: 0.835 - ETA: 2s - loss: 0.5587 - acc: 0.835 - ETA: 1s - loss: 0.5594 - acc: 0.835 - ETA: 1s - loss: 0.5588 - acc: 0.835 - ETA: 0s - loss: 0.5580 - acc: 0.8357Epoch 00145: val_loss did not improve 6680/6680 [==============================] - 219s - loss: 0.5580 - acc: 0.8358 - val_loss: 7.5189 - val_acc: 0.0623 Epoch 147/200 4120/6680 [=================>............] - ETA: 211s - loss: 0.6624 - acc: 0.850 - ETA: 208s - loss: 0.5040 - acc: 0.850 - ETA: 205s - loss: 0.5430 - acc: 0.833 - ETA: 206s - loss: 0.5291 - acc: 0.837 - ETA: 209s - loss: 0.6239 - acc: 0.820 - ETA: 210s - loss: 0.5804 - acc: 0.833 - ETA: 210s - loss: 0.6163 - acc: 0.835 - ETA: 208s - loss: 0.5606 - acc: 0.856 - ETA: 207s - loss: 0.5649 - acc: 0.861 - ETA: 207s - loss: 0.5451 - acc: 0.870 - ETA: 206s - loss: 0.5388 - acc: 0.863 - ETA: 204s - loss: 0.5168 - acc: 0.866 - ETA: 204s - loss: 0.5164 - acc: 0.861 - ETA: 203s - loss: 0.4910 - acc: 0.867 - ETA: 202s - loss: 0.5030 - acc: 0.860 - ETA: 202s - loss: 0.4868 - acc: 0.865 - ETA: 201s - loss: 0.4730 - acc: 0.870 - ETA: 200s - loss: 0.4839 - acc: 0.869 - ETA: 200s - loss: 0.4973 - acc: 0.860 - ETA: 199s - loss: 0.4978 - acc: 0.857 - ETA: 198s - loss: 0.5002 - acc: 0.854 - ETA: 198s - loss: 0.5006 - acc: 0.852 - 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ETA: 5s - loss: 0.5472 - acc: 0.838 - ETA: 5s - loss: 0.5461 - acc: 0.839 - ETA: 4s - loss: 0.5458 - acc: 0.839 - ETA: 3s - loss: 0.5464 - acc: 0.838 - ETA: 3s - loss: 0.5458 - acc: 0.839 - ETA: 2s - loss: 0.5448 - acc: 0.839 - ETA: 1s - loss: 0.5436 - acc: 0.839 - ETA: 1s - loss: 0.5432 - acc: 0.840 - ETA: 0s - loss: 0.5437 - acc: 0.8401Epoch 00147: val_loss did not improve 6680/6680 [==============================] - 221s - loss: 0.5460 - acc: 0.8398 - val_loss: 7.0522 - val_acc: 0.0539 Epoch 149/200 4120/6680 [=================>............] - ETA: 200s - loss: 0.4421 - acc: 0.900 - ETA: 200s - loss: 0.3744 - acc: 0.925 - ETA: 200s - loss: 0.3956 - acc: 0.933 - ETA: 203s - loss: 0.5368 - acc: 0.875 - ETA: 202s - loss: 0.5475 - acc: 0.870 - ETA: 200s - loss: 0.4922 - acc: 0.883 - ETA: 201s - loss: 0.5529 - acc: 0.864 - ETA: 203s - loss: 0.5577 - acc: 0.862 - ETA: 203s - loss: 0.5320 - acc: 0.866 - ETA: 203s - loss: 0.5225 - acc: 0.865 - ETA: 202s - loss: 0.5034 - acc: 0.868 - ETA: 202s - 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acc: 0.8480Epoch 00150: val_loss did not improve 6680/6680 [==============================] - 221s - loss: 0.5065 - acc: 0.8484 - val_loss: 7.7837 - val_acc: 0.0647 Epoch 152/200 4120/6680 [=================>............] - ETA: 196s - loss: 0.5530 - acc: 0.850 - ETA: 202s - loss: 0.6123 - acc: 0.825 - ETA: 203s - loss: 0.5370 - acc: 0.850 - ETA: 204s - loss: 0.6343 - acc: 0.825 - ETA: 201s - loss: 0.5841 - acc: 0.830 - ETA: 201s - loss: 0.5675 - acc: 0.833 - ETA: 201s - loss: 0.5322 - acc: 0.842 - ETA: 199s - loss: 0.4817 - acc: 0.862 - ETA: 198s - loss: 0.5041 - acc: 0.850 - ETA: 198s - loss: 0.5151 - acc: 0.850 - ETA: 198s - loss: 0.5645 - acc: 0.836 - ETA: 198s - loss: 0.5812 - acc: 0.829 - ETA: 199s - loss: 0.6217 - acc: 0.815 - ETA: 198s - loss: 0.6468 - acc: 0.810 - ETA: 197s - loss: 0.6368 - acc: 0.810 - ETA: 197s - loss: 0.6339 - acc: 0.806 - ETA: 196s - loss: 0.6218 - acc: 0.811 - ETA: 195s - loss: 0.6047 - acc: 0.813 - ETA: 195s - loss: 0.5960 - acc: 0.813 - ETA: 195s - loss: 0.5922 - 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loss: 0.5098 - acc: 0.84 - ETA: 64s - loss: 0.5108 - acc: 0.84 - ETA: 64s - loss: 0.5103 - acc: 0.84 - ETA: 63s - loss: 0.5090 - acc: 0.84 - ETA: 63s - loss: 0.5127 - acc: 0.84 - ETA: 62s - loss: 0.5110 - acc: 0.84 - ETA: 61s - loss: 0.5102 - acc: 0.84 - ETA: 61s - loss: 0.5085 - acc: 0.84 - ETA: 60s - loss: 0.5089 - acc: 0.84 - ETA: 60s - loss: 0.5096 - acc: 0.84 - ETA: 59s - loss: 0.5083 - acc: 0.84 - ETA: 59s - loss: 0.5098 - acc: 0.84 - ETA: 58s - loss: 0.5096 - acc: 0.84 - ETA: 57s - loss: 0.5109 - acc: 0.84 - ETA: 57s - loss: 0.5102 - acc: 0.84 - ETA: 56s - loss: 0.5122 - acc: 0.84 - ETA: 56s - loss: 0.5115 - acc: 0.84 - ETA: 55s - loss: 0.5103 - acc: 0.84 - ETA: 55s - loss: 0.5097 - acc: 0.84 - ETA: 54s - loss: 0.5087 - acc: 0.84 - ETA: 53s - loss: 0.5095 - acc: 0.84 - ETA: 53s - loss: 0.5091 - acc: 0.84 - ETA: 52s - loss: 0.5083 - acc: 0.84 - ETA: 52s - loss: 0.5091 - acc: 0.84 - ETA: 51s - loss: 0.5089 - acc: 0.84 - ETA: 50s - loss: 0.5076 - acc: 0.84 - ETA: 50s - loss: 0.5084 - 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acc: 0.84 - ETA: 19s - loss: 0.5123 - acc: 0.84 - ETA: 18s - loss: 0.5111 - acc: 0.84 - ETA: 17s - loss: 0.5100 - acc: 0.84 - ETA: 17s - loss: 0.5098 - acc: 0.84 - ETA: 16s - loss: 0.5086 - acc: 0.84 - ETA: 16s - loss: 0.5099 - acc: 0.84 - ETA: 15s - loss: 0.5113 - acc: 0.84 - ETA: 14s - loss: 0.5115 - acc: 0.84 - ETA: 14s - loss: 0.5127 - acc: 0.84 - ETA: 13s - loss: 0.5122 - acc: 0.84 - ETA: 13s - loss: 0.5133 - acc: 0.84 - ETA: 12s - loss: 0.5125 - acc: 0.84 - ETA: 12s - loss: 0.5117 - acc: 0.84 - ETA: 11s - loss: 0.5129 - acc: 0.84 - ETA: 10s - loss: 0.5142 - acc: 0.84 - ETA: 10s - loss: 0.5131 - acc: 0.84 - ETA: 9s - loss: 0.5126 - acc: 0.8410 - ETA: 9s - loss: 0.5153 - acc: 0.840 - ETA: 8s - loss: 0.5168 - acc: 0.840 - ETA: 8s - loss: 0.5174 - acc: 0.839 - ETA: 7s - loss: 0.5162 - acc: 0.839 - ETA: 6s - loss: 0.5165 - acc: 0.839 - ETA: 6s - loss: 0.5161 - acc: 0.839 - ETA: 5s - loss: 0.5167 - acc: 0.839 - ETA: 5s - loss: 0.5165 - acc: 0.839 - ETA: 4s - loss: 0.5158 - acc: 0.839 - ETA: 4s - loss: 0.5160 - acc: 0.839 - ETA: 3s - loss: 0.5165 - acc: 0.839 - ETA: 2s - loss: 0.5166 - acc: 0.839 - ETA: 2s - loss: 0.5160 - acc: 0.839 - ETA: 1s - loss: 0.5152 - acc: 0.840 - ETA: 1s - loss: 0.5164 - acc: 0.839 - ETA: 0s - loss: 0.5153 - acc: 0.8402Epoch 00162: val_loss did not improve 6680/6680 [==============================] - 203s - loss: 0.5153 - acc: 0.8401 - val_loss: 7.5493 - val_acc: 0.0623 Epoch 164/200 4120/6680 [=================>............] - ETA: 186s - loss: 0.1246 - acc: 0.900 - ETA: 187s - loss: 0.1980 - acc: 0.875 - ETA: 184s - loss: 0.5319 - acc: 0.833 - ETA: 189s - loss: 0.5325 - acc: 0.812 - ETA: 189s - loss: 0.5317 - acc: 0.820 - ETA: 188s - loss: 0.4783 - acc: 0.841 - ETA: 187s - loss: 0.5376 - acc: 0.842 - ETA: 187s - loss: 0.5190 - acc: 0.843 - ETA: 187s - loss: 0.5013 - acc: 0.850 - ETA: 185s - loss: 0.4750 - acc: 0.855 - ETA: 187s - loss: 0.4654 - acc: 0.854 - ETA: 187s - loss: 0.4418 - acc: 0.862 - ETA: 185s - loss: 0.4629 - acc: 0.861 - ETA: 185s - 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ETA: 7s - loss: 0.4547 - acc: 0.863 - ETA: 6s - loss: 0.4565 - acc: 0.862 - ETA: 6s - loss: 0.4576 - acc: 0.862 - ETA: 5s - loss: 0.4581 - acc: 0.862 - ETA: 4s - loss: 0.4584 - acc: 0.862 - ETA: 4s - loss: 0.4579 - acc: 0.862 - ETA: 3s - loss: 0.4581 - acc: 0.861 - ETA: 3s - loss: 0.4585 - acc: 0.861 - ETA: 2s - loss: 0.4596 - acc: 0.861 - ETA: 1s - loss: 0.4593 - acc: 0.861 - ETA: 1s - loss: 0.4584 - acc: 0.861 - ETA: 0s - loss: 0.4577 - acc: 0.8620Epoch 00163: val_loss did not improve 6680/6680 [==============================] - 214s - loss: 0.4584 - acc: 0.8620 - val_loss: 7.0640 - val_acc: 0.0599 Epoch 165/200 4120/6680 [=================>............] - ETA: 191s - loss: 0.9396 - acc: 0.750 - ETA: 191s - loss: 0.5792 - acc: 0.825 - ETA: 190s - loss: 0.4528 - acc: 0.866 - ETA: 187s - loss: 0.4537 - acc: 0.862 - ETA: 193s - loss: 0.4903 - acc: 0.840 - ETA: 193s - loss: 0.4479 - acc: 0.850 - ETA: 199s - loss: 0.4837 - acc: 0.850 - ETA: 201s - loss: 0.5666 - acc: 0.837 - ETA: 199s - loss: 0.6284 - 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ETA: 9s - loss: 0.5093 - acc: 0.8524 - ETA: 9s - loss: 0.5093 - acc: 0.852 - ETA: 8s - loss: 0.5109 - acc: 0.852 - ETA: 8s - loss: 0.5106 - acc: 0.852 - ETA: 7s - loss: 0.5109 - acc: 0.852 - ETA: 7s - loss: 0.5101 - acc: 0.852 - ETA: 6s - loss: 0.5090 - acc: 0.853 - ETA: 5s - loss: 0.5092 - acc: 0.852 - ETA: 5s - loss: 0.5090 - acc: 0.852 - ETA: 4s - loss: 0.5085 - acc: 0.853 - ETA: 4s - loss: 0.5101 - acc: 0.852 - ETA: 3s - loss: 0.5118 - acc: 0.852 - ETA: 2s - loss: 0.5139 - acc: 0.851 - ETA: 2s - loss: 0.5138 - acc: 0.852 - ETA: 1s - loss: 0.5136 - acc: 0.852 - ETA: 1s - loss: 0.5133 - acc: 0.852 - ETA: 0s - loss: 0.5144 - acc: 0.8517Epoch 00164: val_loss did not improve 6680/6680 [==============================] - 207s - loss: 0.5151 - acc: 0.8515 - val_loss: 7.2775 - val_acc: 0.0551 Epoch 166/200 4120/6680 [=================>............] - ETA: 189s - loss: 0.1674 - acc: 1.000 - ETA: 182s - loss: 0.3663 - acc: 0.925 - ETA: 184s - loss: 0.3926 - acc: 0.900 - ETA: 181s - loss: 0.4331 - 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ETA: 7s - loss: 0.4661 - acc: 0.858 - ETA: 6s - loss: 0.4653 - acc: 0.859 - ETA: 6s - loss: 0.4641 - acc: 0.859 - ETA: 5s - loss: 0.4634 - acc: 0.859 - ETA: 4s - loss: 0.4657 - acc: 0.859 - ETA: 4s - loss: 0.4653 - acc: 0.859 - ETA: 3s - loss: 0.4675 - acc: 0.859 - ETA: 3s - loss: 0.4676 - acc: 0.858 - ETA: 2s - loss: 0.4671 - acc: 0.858 - ETA: 1s - loss: 0.4677 - acc: 0.858 - ETA: 1s - loss: 0.4677 - acc: 0.858 - ETA: 0s - loss: 0.4675 - acc: 0.8586Epoch 00179: val_loss did not improve 6680/6680 [==============================] - 215s - loss: 0.4679 - acc: 0.8587 - val_loss: 7.4791 - val_acc: 0.0539 Epoch 181/200 4120/6680 [=================>............] - ETA: 202s - loss: 0.3210 - acc: 0.900 - ETA: 198s - loss: 0.3083 - acc: 0.900 - ETA: 193s - loss: 0.3033 - acc: 0.916 - ETA: 193s - loss: 0.3584 - acc: 0.912 - ETA: 192s - loss: 0.3630 - acc: 0.890 - ETA: 192s - loss: 0.3853 - acc: 0.883 - ETA: 191s - loss: 0.3980 - acc: 0.878 - ETA: 191s - loss: 0.4319 - acc: 0.881 - ETA: 191s - loss: 0.4323 - 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ETA: 10s - loss: 0.4742 - acc: 0.86 - ETA: 9s - loss: 0.4754 - acc: 0.8596 - ETA: 9s - loss: 0.4755 - acc: 0.859 - ETA: 8s - loss: 0.4748 - acc: 0.859 - ETA: 8s - loss: 0.4737 - acc: 0.860 - ETA: 7s - loss: 0.4729 - acc: 0.860 - ETA: 6s - loss: 0.4729 - acc: 0.860 - ETA: 6s - loss: 0.4737 - acc: 0.859 - ETA: 5s - loss: 0.4741 - acc: 0.859 - ETA: 5s - loss: 0.4727 - acc: 0.860 - ETA: 4s - loss: 0.4733 - acc: 0.860 - ETA: 3s - loss: 0.4733 - acc: 0.860 - ETA: 3s - loss: 0.4724 - acc: 0.860 - ETA: 2s - loss: 0.4743 - acc: 0.859 - ETA: 1s - loss: 0.4763 - acc: 0.859 - ETA: 1s - loss: 0.4761 - acc: 0.859 - ETA: 0s - loss: 0.4763 - acc: 0.8589Epoch 00180: val_loss did not improve 6680/6680 [==============================] - 219s - loss: 0.4771 - acc: 0.8585 - val_loss: 7.5458 - val_acc: 0.0575 Epoch 182/200 4120/6680 [=================>............] - ETA: 190s - loss: 0.3240 - acc: 0.850 - ETA: 192s - loss: 0.3905 - acc: 0.850 - ETA: 188s - loss: 0.3908 - acc: 0.866 - ETA: 191s - loss: 0.3544 - acc: 0.875 - ETA: 193s - loss: 0.2985 - acc: 0.900 - ETA: 191s - loss: 0.3032 - acc: 0.900 - ETA: 191s - loss: 0.3301 - acc: 0.900 - ETA: 191s - loss: 0.3190 - acc: 0.887 - ETA: 191s - loss: 0.3183 - acc: 0.888 - ETA: 192s - loss: 0.3526 - acc: 0.895 - ETA: 191s - loss: 0.3242 - acc: 0.904 - ETA: 191s - loss: 0.3183 - acc: 0.900 - ETA: 190s - loss: 0.3316 - acc: 0.888 - ETA: 190s - loss: 0.3449 - acc: 0.882 - ETA: 190s - loss: 0.3247 - acc: 0.890 - ETA: 189s - loss: 0.3315 - acc: 0.887 - ETA: 188s - loss: 0.3276 - acc: 0.888 - ETA: 187s - loss: 0.3317 - acc: 0.886 - ETA: 187s - loss: 0.3426 - acc: 0.881 - ETA: 186s - loss: 0.3543 - acc: 0.877 - ETA: 185s - loss: 0.3799 - acc: 0.871 - ETA: 185s - loss: 0.3967 - acc: 0.868 - ETA: 185s - loss: 0.4130 - acc: 0.863 - ETA: 189s - loss: 0.4197 - acc: 0.860 - ETA: 189s - loss: 0.4308 - acc: 0.856 - ETA: 189s - loss: 0.4289 - acc: 0.859 - ETA: 189s - loss: 0.4248 - acc: 0.859 - ETA: 188s - loss: 0.4273 - acc: 0.858 - ETA: 187s - loss: 0.4373 - 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acc: 0.86 - ETA: 13s - loss: 0.4675 - acc: 0.86 - ETA: 12s - loss: 0.4675 - acc: 0.86 - ETA: 12s - loss: 0.4680 - acc: 0.86 - ETA: 11s - loss: 0.4683 - acc: 0.86 - ETA: 11s - loss: 0.4671 - acc: 0.86 - ETA: 10s - loss: 0.4674 - acc: 0.86 - ETA: 9s - loss: 0.4665 - acc: 0.8643 - ETA: 9s - loss: 0.4660 - acc: 0.864 - ETA: 8s - loss: 0.4665 - acc: 0.864 - ETA: 7s - loss: 0.4655 - acc: 0.864 - ETA: 7s - loss: 0.4674 - acc: 0.864 - ETA: 6s - loss: 0.4667 - acc: 0.864 - ETA: 6s - loss: 0.4664 - acc: 0.864 - ETA: 5s - loss: 0.4659 - acc: 0.864 - ETA: 4s - loss: 0.4663 - acc: 0.864 - ETA: 4s - loss: 0.4663 - acc: 0.864 - ETA: 3s - loss: 0.4668 - acc: 0.864 - ETA: 3s - loss: 0.4677 - acc: 0.863 - ETA: 2s - loss: 0.4689 - acc: 0.863 - ETA: 1s - loss: 0.4683 - acc: 0.863 - ETA: 1s - loss: 0.4694 - acc: 0.863 - ETA: 0s - loss: 0.4713 - acc: 0.8626Epoch 00181: val_loss did not improve 6680/6680 [==============================] - 219s - loss: 0.4709 - acc: 0.8626 - val_loss: 7.8465 - val_acc: 0.0671 Epoch 183/200 4120/6680 [=================>............] - 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ETA: 3s - loss: 0.4724 - acc: 0.859 - ETA: 3s - loss: 0.4713 - acc: 0.860 - ETA: 2s - loss: 0.4717 - acc: 0.859 - ETA: 2s - loss: 0.4707 - acc: 0.860 - ETA: 1s - loss: 0.4702 - acc: 0.860 - ETA: 1s - loss: 0.4698 - acc: 0.860 - ETA: 0s - loss: 0.4698 - acc: 0.8605Epoch 00196: val_loss did not improve 6680/6680 [==============================] - 198s - loss: 0.4709 - acc: 0.8605 - val_loss: 7.8102 - val_acc: 0.0647 Epoch 198/200 4140/6680 [=================>............] - ETA: 185s - loss: 1.1875 - acc: 0.700 - ETA: 180s - loss: 0.8428 - acc: 0.800 - ETA: 178s - loss: 0.7352 - acc: 0.800 - ETA: 177s - loss: 0.7363 - acc: 0.800 - ETA: 176s - loss: 0.6791 - acc: 0.810 - ETA: 176s - loss: 0.7104 - acc: 0.800 - ETA: 175s - loss: 0.6680 - acc: 0.821 - ETA: 174s - loss: 0.6805 - acc: 0.818 - ETA: 173s - loss: 0.6830 - acc: 0.816 - ETA: 173s - loss: 0.6671 - acc: 0.815 - ETA: 172s - loss: 0.6182 - acc: 0.827 - ETA: 172s - loss: 0.5885 - acc: 0.837 - ETA: 171s - loss: 0.5778 - acc: 0.838 - ETA: 170s - 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ETA: 8s - loss: 0.4686 - acc: 0.861 - ETA: 8s - loss: 0.4684 - acc: 0.861 - ETA: 7s - loss: 0.4684 - acc: 0.861 - ETA: 7s - loss: 0.4696 - acc: 0.861 - ETA: 6s - loss: 0.4686 - acc: 0.861 - ETA: 6s - loss: 0.4680 - acc: 0.861 - ETA: 5s - loss: 0.4687 - acc: 0.861 - ETA: 5s - loss: 0.4690 - acc: 0.861 - ETA: 4s - loss: 0.4679 - acc: 0.861 - ETA: 3s - loss: 0.4681 - acc: 0.861 - ETA: 3s - loss: 0.4678 - acc: 0.861 - ETA: 2s - loss: 0.4686 - acc: 0.861 - ETA: 2s - loss: 0.4695 - acc: 0.860 - ETA: 1s - loss: 0.4712 - acc: 0.860 - ETA: 1s - loss: 0.4705 - acc: 0.860 - ETA: 0s - loss: 0.4694 - acc: 0.8613Epoch 00198: val_loss did not improve 6680/6680 [==============================] - 196s - loss: 0.4706 - acc: 0.8611 - val_loss: 7.3791 - val_acc: 0.0443 Epoch 200/200 4140/6680 [=================>............] - ETA: 187s - loss: 0.8323 - acc: 0.750 - ETA: 184s - loss: 0.5600 - acc: 0.825 - ETA: 183s - loss: 0.4680 - acc: 0.850 - ETA: 184s - loss: 0.4617 - acc: 0.825 - ETA: 185s - loss: 0.4627 - 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Out[53]:
<keras.callbacks.History at 0x1b45dfba0f0>
Load the Model with the Best Validation Loss¶
In [55]:
model.load_weights('saved_models/weights.best.from_scratch.hdf5')
Test the Model¶
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.
In [56]:
# get index of predicted dog breed for each image in test set
dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]
# report test accuracy
test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 7.8947%
In [57]:
bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')
train_VGG16 = bottleneck_features['train']
valid_VGG16 = bottleneck_features['valid']
test_VGG16 = bottleneck_features['test']
Model Architecture¶
The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.
In [58]:
VGG16_model = Sequential()
VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))
VGG16_model.add(Dense(133, activation='softmax'))
VGG16_model.summary()
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= global_average_pooling2d_1 ( (None, 512) 0 _________________________________________________________________ dense_13 (Dense) (None, 133) 68229 ================================================================= Total params: 68,229 Trainable params: 68,229 Non-trainable params: 0 _________________________________________________________________
Compile the Model¶
In [59]:
VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
Train the Model¶
In [60]:
checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5',
verbose=1, save_best_only=True)
VGG16_model.fit(train_VGG16, train_targets,
validation_data=(valid_VGG16, valid_targets),
epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)
Train on 6680 samples, validate on 835 samples Epoch 1/20 6660/6680 [============================>.] - ETA: 417s - loss: 15.4531 - acc: 0.0000e+ - ETA: 108s - loss: 14.9236 - acc: 0.0000e+ - ETA: 74s - loss: 14.8387 - acc: 0.0083 - ETA: 51s - loss: 15.0275 - acc: 0.011 - ETA: 43s - loss: 15.1959 - acc: 0.009 - ETA: 34s - loss: 15.1963 - acc: 0.010 - ETA: 29s - loss: 15.1686 - acc: 0.014 - ETA: 24s - loss: 15.1458 - acc: 0.016 - ETA: 23s - loss: 15.0739 - acc: 0.017 - ETA: 20s - loss: 14.9916 - acc: 0.021 - ETA: 19s - loss: 14.9340 - acc: 0.023 - ETA: 18s - loss: 14.8981 - acc: 0.024 - ETA: 17s - loss: 14.8412 - acc: 0.027 - ETA: 16s - loss: 14.8961 - acc: 0.027 - ETA: 15s - loss: 14.8272 - acc: 0.027 - ETA: 14s - loss: 14.8283 - acc: 0.027 - ETA: 13s - loss: 14.8737 - acc: 0.025 - ETA: 13s - loss: 14.8588 - acc: 0.025 - ETA: 12s - loss: 14.8180 - acc: 0.027 - ETA: 12s - loss: 14.7810 - acc: 0.029 - ETA: 12s - loss: 14.7636 - acc: 0.029 - ETA: 12s - loss: 14.7352 - acc: 0.029 - ETA: 11s - 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loss: 14.0425 - acc: 0.05 - ETA: 6s - loss: 14.0385 - acc: 0.05 - ETA: 6s - loss: 14.0175 - acc: 0.05 - ETA: 5s - loss: 14.0008 - acc: 0.05 - ETA: 5s - loss: 13.9712 - acc: 0.05 - ETA: 5s - loss: 13.9563 - acc: 0.05 - ETA: 5s - loss: 13.9327 - acc: 0.05 - ETA: 5s - loss: 13.9344 - acc: 0.05 - ETA: 5s - loss: 13.9315 - acc: 0.05 - ETA: 5s - loss: 13.9192 - acc: 0.05 - ETA: 5s - loss: 13.8783 - acc: 0.06 - ETA: 4s - loss: 13.8615 - acc: 0.06 - ETA: 4s - loss: 13.8313 - acc: 0.06 - ETA: 4s - loss: 13.8107 - acc: 0.06 - ETA: 4s - loss: 13.7960 - acc: 0.06 - ETA: 4s - loss: 13.7812 - acc: 0.06 - ETA: 4s - loss: 13.7691 - acc: 0.06 - ETA: 4s - loss: 13.7398 - acc: 0.06 - ETA: 4s - loss: 13.7058 - acc: 0.06 - ETA: 4s - loss: 13.6608 - acc: 0.06 - ETA: 3s - loss: 13.6139 - acc: 0.07 - ETA: 3s - loss: 13.6011 - acc: 0.07 - ETA: 3s - loss: 13.5665 - acc: 0.07 - ETA: 3s - loss: 13.5506 - acc: 0.07 - ETA: 3s - loss: 13.5444 - acc: 0.07 - ETA: 3s - loss: 13.5014 - acc: 0.07 - ETA: 3s - loss: 13.4875 - acc: 0.07 - ETA: 3s - loss: 13.4761 - acc: 0.07 - ETA: 3s - loss: 13.4528 - acc: 0.07 - ETA: 3s - loss: 13.4383 - acc: 0.08 - ETA: 2s - loss: 13.4095 - acc: 0.08 - ETA: 2s - loss: 13.3973 - acc: 0.08 - ETA: 2s - loss: 13.3815 - acc: 0.08 - ETA: 2s - loss: 13.3744 - acc: 0.08 - ETA: 2s - loss: 13.3737 - acc: 0.08 - ETA: 2s - loss: 13.3543 - acc: 0.08 - ETA: 2s - loss: 13.3315 - acc: 0.08 - ETA: 2s - loss: 13.3194 - acc: 0.08 - ETA: 2s - loss: 13.3293 - acc: 0.08 - ETA: 2s - loss: 13.3038 - acc: 0.08 - ETA: 2s - loss: 13.2775 - acc: 0.08 - ETA: 2s - loss: 13.2534 - acc: 0.09 - ETA: 1s - loss: 13.2401 - acc: 0.09 - ETA: 1s - loss: 13.2268 - acc: 0.09 - ETA: 1s - loss: 13.2115 - acc: 0.09 - ETA: 1s - loss: 13.2046 - acc: 0.09 - ETA: 1s - loss: 13.1878 - acc: 0.09 - ETA: 1s - loss: 13.1839 - acc: 0.09 - ETA: 1s - loss: 13.1687 - acc: 0.09 - ETA: 1s - loss: 13.1596 - acc: 0.09 - ETA: 1s - loss: 13.1496 - acc: 0.09 - ETA: 1s - loss: 13.1451 - acc: 0.09 - ETA: 1s - loss: 13.1289 - acc: 0.09 - ETA: 1s - loss: 13.1225 - acc: 0.09 - ETA: 0s - loss: 13.1185 - acc: 0.09 - ETA: 0s - loss: 13.1145 - acc: 0.09 - ETA: 0s - loss: 13.0854 - acc: 0.10 - ETA: 0s - loss: 13.0784 - acc: 0.10 - ETA: 0s - loss: 13.0586 - acc: 0.10 - ETA: 0s - loss: 13.0360 - acc: 0.10 - ETA: 0s - loss: 13.0354 - acc: 0.10 - ETA: 0s - loss: 13.0138 - acc: 0.10 - ETA: 0s - loss: 13.0042 - acc: 0.10 - ETA: 0s - loss: 12.9825 - acc: 0.10 - ETA: 0s - loss: 12.9711 - acc: 0.10 - ETA: 0s - loss: 12.9543 - acc: 0.10 - ETA: 0s - loss: 12.9321 - acc: 0.10 - ETA: 0s - loss: 12.9246 - acc: 0.1095Epoch 00000: val_loss improved from inf to 11.66087, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 9s - loss: 12.9206 - acc: 0.1097 - val_loss: 11.6609 - val_acc: 0.1832 Epoch 2/20 6600/6680 [============================>.] - ETA: 5s - loss: 10.2524 - acc: 0.20 - ETA: 5s - loss: 12.0022 - acc: 0.15 - ETA: 6s - loss: 11.7630 - acc: 0.17 - ETA: 7s - loss: 11.7245 - acc: 0.18 - ETA: 7s - loss: 12.0254 - acc: 0.17 - ETA: 6s - loss: 11.6453 - acc: 0.20 - ETA: 6s - loss: 11.6394 - acc: 0.20 - ETA: 6s - loss: 11.6173 - acc: 0.20 - ETA: 6s - loss: 11.6012 - acc: 0.20 - ETA: 7s - loss: 11.5987 - acc: 0.20 - ETA: 6s - loss: 11.5291 - acc: 0.21 - ETA: 6s - loss: 11.4982 - acc: 0.21 - ETA: 6s - loss: 11.4192 - acc: 0.21 - ETA: 6s - loss: 11.4257 - acc: 0.20 - ETA: 6s - loss: 11.4162 - acc: 0.20 - ETA: 6s - loss: 11.3222 - acc: 0.21 - ETA: 6s - loss: 11.2723 - acc: 0.21 - ETA: 6s - loss: 11.3304 - acc: 0.21 - ETA: 6s - loss: 11.3751 - acc: 0.21 - ETA: 6s - loss: 11.3003 - acc: 0.21 - ETA: 6s - loss: 11.3192 - acc: 0.21 - ETA: 6s - loss: 11.3531 - acc: 0.21 - ETA: 6s - loss: 11.4312 - acc: 0.21 - ETA: 6s - loss: 11.4460 - acc: 0.21 - ETA: 5s - loss: 11.4048 - acc: 0.21 - ETA: 6s - loss: 11.3892 - acc: 0.21 - ETA: 5s - loss: 11.4132 - acc: 0.21 - ETA: 5s - loss: 11.4049 - acc: 0.21 - ETA: 5s - loss: 11.3201 - acc: 0.22 - ETA: 5s - loss: 11.2730 - acc: 0.22 - ETA: 5s - loss: 11.2437 - acc: 0.22 - ETA: 5s - loss: 11.2700 - acc: 0.22 - ETA: 5s - loss: 11.2620 - acc: 0.22 - ETA: 5s - loss: 11.2606 - acc: 0.22 - ETA: 5s - loss: 11.2828 - acc: 0.22 - ETA: 5s - loss: 11.3286 - acc: 0.21 - ETA: 5s - loss: 11.3355 - acc: 0.21 - ETA: 5s - loss: 11.3226 - acc: 0.21 - ETA: 5s - loss: 11.2671 - acc: 0.22 - ETA: 4s - loss: 11.2737 - acc: 0.22 - ETA: 4s - loss: 11.2798 - acc: 0.21 - ETA: 4s - loss: 11.2434 - acc: 0.22 - ETA: 4s - loss: 11.2557 - acc: 0.22 - ETA: 4s - loss: 11.2620 - acc: 0.22 - ETA: 4s - loss: 11.2633 - acc: 0.22 - ETA: 4s - loss: 11.2611 - acc: 0.22 - ETA: 4s - loss: 11.2450 - acc: 0.22 - ETA: 4s - loss: 11.2329 - acc: 0.22 - ETA: 4s - loss: 11.2400 - acc: 0.22 - ETA: 4s - loss: 11.2080 - acc: 0.22 - ETA: 4s - loss: 11.2315 - acc: 0.22 - ETA: 4s - loss: 11.2057 - acc: 0.22 - ETA: 4s - loss: 11.2047 - acc: 0.22 - ETA: 4s - loss: 11.1840 - acc: 0.22 - ETA: 4s - loss: 11.2015 - acc: 0.22 - ETA: 4s - loss: 11.2107 - acc: 0.22 - ETA: 3s - loss: 11.2102 - acc: 0.22 - ETA: 3s - loss: 11.1984 - acc: 0.22 - ETA: 3s - loss: 11.1674 - acc: 0.22 - ETA: 3s - loss: 11.1277 - acc: 0.22 - ETA: 3s - loss: 11.1076 - acc: 0.23 - ETA: 3s - loss: 11.1285 - acc: 0.22 - ETA: 3s - loss: 11.1236 - acc: 0.23 - ETA: 3s - loss: 11.1352 - acc: 0.22 - ETA: 3s - loss: 11.1252 - acc: 0.22 - ETA: 3s - loss: 11.1148 - acc: 0.23 - ETA: 3s - loss: 11.1150 - acc: 0.23 - ETA: 3s - loss: 11.1038 - acc: 0.23 - ETA: 3s - loss: 11.0593 - acc: 0.23 - ETA: 3s - loss: 11.0436 - acc: 0.23 - ETA: 2s - loss: 11.0399 - acc: 0.23 - ETA: 2s - loss: 11.0204 - acc: 0.23 - ETA: 2s - loss: 11.0123 - acc: 0.23 - ETA: 2s - loss: 10.9889 - acc: 0.23 - ETA: 2s - loss: 10.9628 - acc: 0.23 - ETA: 2s - loss: 10.9622 - acc: 0.23 - ETA: 2s - loss: 10.9722 - acc: 0.23 - ETA: 2s - loss: 10.9749 - acc: 0.23 - ETA: 1s - loss: 10.9487 - acc: 0.24 - ETA: 1s - loss: 10.9340 - acc: 0.24 - ETA: 1s - loss: 10.9319 - acc: 0.24 - ETA: 1s - loss: 10.9091 - acc: 0.24 - ETA: 1s - loss: 10.9330 - acc: 0.24 - ETA: 1s - loss: 10.9295 - acc: 0.24 - ETA: 1s - loss: 10.9104 - acc: 0.24 - ETA: 1s - loss: 10.9103 - acc: 0.24 - ETA: 1s - loss: 10.8960 - acc: 0.24 - ETA: 0s - loss: 10.9128 - acc: 0.24 - ETA: 0s - loss: 10.9304 - acc: 0.24 - ETA: 0s - loss: 10.9338 - acc: 0.24 - ETA: 0s - loss: 10.9348 - acc: 0.24 - ETA: 0s - loss: 10.9325 - acc: 0.24 - ETA: 0s - loss: 10.9396 - acc: 0.24 - ETA: 0s - loss: 10.9248 - acc: 0.24 - ETA: 0s - loss: 10.9251 - acc: 0.24 - ETA: 0s - loss: 10.9194 - acc: 0.24 - ETA: 0s - loss: 10.9084 - acc: 0.24 - ETA: 0s - loss: 10.8908 - acc: 0.24 - ETA: 0s - loss: 10.8778 - acc: 0.2461Epoch 00001: val_loss improved from 11.66087 to 10.73332, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 5s - loss: 10.8750 - acc: 0.2464 - val_loss: 10.7333 - val_acc: 0.2383 Epoch 3/20 6640/6680 [============================>.] - ETA: 3s - loss: 11.3365 - acc: 0.25 - ETA: 3s - loss: 10.0070 - acc: 0.30 - ETA: 3s - loss: 10.2400 - acc: 0.29 - ETA: 3s - loss: 10.2267 - acc: 0.28 - ETA: 3s - loss: 10.3794 - acc: 0.28 - ETA: 3s - loss: 10.2043 - acc: 0.29 - ETA: 3s - loss: 10.3634 - acc: 0.28 - ETA: 3s - loss: 10.3882 - acc: 0.28 - ETA: 2s - loss: 10.2482 - acc: 0.29 - ETA: 2s - loss: 10.2812 - acc: 0.29 - ETA: 2s - loss: 10.3102 - acc: 0.29 - ETA: 2s - loss: 10.3238 - acc: 0.29 - ETA: 2s - loss: 10.2680 - acc: 0.30 - ETA: 2s - loss: 10.2680 - acc: 0.30 - ETA: 2s - loss: 10.2510 - acc: 0.30 - ETA: 2s - loss: 10.3434 - acc: 0.29 - ETA: 2s - loss: 10.3749 - acc: 0.29 - ETA: 2s - loss: 10.3153 - acc: 0.30 - ETA: 2s - loss: 10.3268 - acc: 0.30 - ETA: 2s - loss: 10.3109 - acc: 0.30 - ETA: 1s - loss: 10.2733 - acc: 0.30 - ETA: 1s - loss: 10.2372 - acc: 0.30 - ETA: 1s - loss: 10.2588 - acc: 0.30 - ETA: 1s - loss: 10.2633 - acc: 0.30 - ETA: 1s - loss: 10.2518 - acc: 0.30 - ETA: 1s - loss: 10.3009 - acc: 0.30 - ETA: 1s - loss: 10.2837 - acc: 0.30 - ETA: 1s - loss: 10.2843 - acc: 0.30 - ETA: 1s - loss: 10.2671 - acc: 0.30 - ETA: 1s - loss: 10.2585 - acc: 0.30 - ETA: 1s - loss: 10.2715 - acc: 0.30 - ETA: 1s - loss: 10.2663 - acc: 0.30 - ETA: 1s - loss: 10.2684 - acc: 0.30 - ETA: 1s - loss: 10.2628 - acc: 0.30 - ETA: 1s - loss: 10.2854 - acc: 0.30 - ETA: 1s - loss: 10.3187 - acc: 0.30 - ETA: 0s - loss: 10.3145 - acc: 0.30 - ETA: 0s - loss: 10.3029 - acc: 0.30 - ETA: 0s - loss: 10.3021 - acc: 0.30 - ETA: 0s - loss: 10.3023 - acc: 0.30 - ETA: 0s - loss: 10.3113 - acc: 0.30 - ETA: 0s - loss: 10.2977 - acc: 0.30 - ETA: 0s - loss: 10.2675 - acc: 0.30 - ETA: 0s - loss: 10.2738 - acc: 0.30 - ETA: 0s - loss: 10.2596 - acc: 0.30 - ETA: 0s - loss: 10.2392 - acc: 0.30 - ETA: 0s - loss: 10.2270 - acc: 0.30 - ETA: 0s - loss: 10.2303 - acc: 0.30 - ETA: 0s - loss: 10.2465 - acc: 0.30 - ETA: 0s - loss: 10.2612 - acc: 0.30 - ETA: 0s - loss: 10.2579 - acc: 0.30 - ETA: 0s - loss: 10.2714 - acc: 0.30 - ETA: 0s - loss: 10.2597 - acc: 0.30 - ETA: 0s - loss: 10.2549 - acc: 0.30 - ETA: 0s - loss: 10.2647 - acc: 0.3075Epoch 00002: val_loss improved from 10.73332 to 10.47000, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 3s - loss: 10.2595 - acc: 0.3078 - val_loss: 10.4700 - val_acc: 0.2659 Epoch 4/20 6660/6680 [============================>.] - ETA: 2s - loss: 10.7438 - acc: 0.30 - ETA: 2s - loss: 9.7927 - acc: 0.3500 - ETA: 3s - loss: 10.0549 - acc: 0.33 - ETA: 2s - loss: 9.8686 - acc: 0.3500 - ETA: 2s - loss: 10.0281 - acc: 0.33 - ETA: 2s - loss: 9.8877 - acc: 0.3517 - ETA: 2s - loss: 9.7687 - acc: 0.357 - ETA: 2s - loss: 9.9796 - acc: 0.347 - ETA: 2s - loss: 10.0206 - acc: 0.34 - ETA: 2s - loss: 10.0400 - acc: 0.34 - ETA: 2s - loss: 9.9439 - acc: 0.3491 - ETA: 2s - loss: 9.9897 - acc: 0.345 - ETA: 2s - loss: 10.0179 - acc: 0.34 - ETA: 2s - loss: 10.0813 - acc: 0.33 - ETA: 2s - loss: 10.0976 - acc: 0.33 - ETA: 2s - loss: 10.1070 - acc: 0.33 - ETA: 2s - loss: 10.1068 - acc: 0.33 - ETA: 2s - loss: 10.1187 - acc: 0.33 - ETA: 2s - loss: 10.2094 - acc: 0.33 - ETA: 2s - loss: 10.1966 - acc: 0.33 - ETA: 2s - loss: 10.1925 - acc: 0.33 - ETA: 2s - loss: 10.2250 - acc: 0.33 - ETA: 2s - loss: 10.1978 - acc: 0.33 - ETA: 2s - loss: 10.1996 - acc: 0.33 - ETA: 2s - loss: 10.2431 - acc: 0.32 - ETA: 1s - loss: 10.2215 - acc: 0.32 - ETA: 1s - loss: 10.2036 - acc: 0.32 - ETA: 1s - loss: 10.1928 - acc: 0.32 - ETA: 1s - loss: 10.2154 - acc: 0.32 - ETA: 1s - loss: 10.2383 - acc: 0.32 - ETA: 1s - loss: 10.2509 - acc: 0.32 - ETA: 1s - loss: 10.2245 - acc: 0.32 - ETA: 1s - loss: 10.1931 - acc: 0.32 - ETA: 1s - loss: 10.1622 - acc: 0.33 - ETA: 1s - loss: 10.1603 - acc: 0.33 - ETA: 1s - loss: 10.1470 - acc: 0.33 - ETA: 1s - loss: 10.1070 - acc: 0.33 - ETA: 1s - loss: 10.1245 - acc: 0.33 - ETA: 1s - loss: 10.1095 - acc: 0.33 - ETA: 1s - loss: 10.0938 - acc: 0.33 - ETA: 1s - loss: 10.0996 - acc: 0.33 - ETA: 0s - loss: 10.1032 - acc: 0.33 - ETA: 0s - loss: 10.0973 - acc: 0.33 - ETA: 0s - loss: 10.0791 - acc: 0.33 - ETA: 0s - loss: 10.0441 - acc: 0.34 - ETA: 0s - loss: 10.0560 - acc: 0.33 - ETA: 0s - loss: 10.0395 - acc: 0.34 - ETA: 0s - loss: 10.0170 - acc: 0.34 - ETA: 0s - loss: 10.0060 - acc: 0.34 - ETA: 0s - loss: 9.9987 - acc: 0.3427 - ETA: 0s - loss: 9.9831 - acc: 0.343 - ETA: 0s - loss: 10.0030 - acc: 0.34 - ETA: 0s - loss: 10.0126 - acc: 0.34 - ETA: 0s - loss: 10.0250 - acc: 0.34 - ETA: 0s - loss: 10.0361 - acc: 0.34 - ETA: 0s - loss: 10.0511 - acc: 0.34 - ETA: 0s - loss: 10.0545 - acc: 0.33 - ETA: 0s - loss: 10.0450 - acc: 0.3408Epoch 00003: val_loss improved from 10.47000 to 10.32665, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 3s - loss: 10.0562 - acc: 0.3401 - val_loss: 10.3266 - val_acc: 0.2934 Epoch 5/20 6620/6680 [============================>.] - ETA: 2s - loss: 8.9632 - acc: 0.400 - ETA: 2s - loss: 9.5207 - acc: 0.381 - ETA: 2s - loss: 10.2509 - acc: 0.33 - ETA: 2s - loss: 10.3105 - acc: 0.33 - ETA: 2s - loss: 10.1001 - acc: 0.34 - ETA: 2s - loss: 10.0739 - acc: 0.34 - ETA: 2s - loss: 9.9058 - acc: 0.3535 - ETA: 2s - loss: 10.0162 - acc: 0.34 - ETA: 2s - loss: 10.0680 - acc: 0.34 - ETA: 2s - loss: 9.9692 - acc: 0.3523 - ETA: 2s - loss: 9.9722 - acc: 0.350 - ETA: 1s - loss: 9.9922 - acc: 0.350 - ETA: 1s - loss: 10.0444 - acc: 0.34 - ETA: 1s - loss: 10.1228 - acc: 0.34 - ETA: 1s - loss: 10.0402 - acc: 0.34 - ETA: 1s - loss: 9.9935 - acc: 0.3462 - ETA: 1s - loss: 9.9949 - acc: 0.347 - ETA: 1s - loss: 10.0655 - acc: 0.34 - ETA: 1s - loss: 10.0242 - acc: 0.34 - ETA: 1s - loss: 10.0584 - acc: 0.34 - ETA: 1s - loss: 10.0683 - acc: 0.34 - ETA: 1s - loss: 10.0858 - acc: 0.34 - ETA: 1s - loss: 10.1111 - acc: 0.34 - ETA: 1s - loss: 10.0752 - acc: 0.34 - ETA: 1s - loss: 10.1147 - acc: 0.34 - ETA: 1s - loss: 10.0919 - acc: 0.34 - ETA: 1s - loss: 10.0645 - acc: 0.34 - ETA: 1s - loss: 10.0622 - acc: 0.34 - ETA: 1s - loss: 10.0309 - acc: 0.34 - ETA: 1s - loss: 10.0373 - acc: 0.34 - ETA: 1s - loss: 10.0203 - acc: 0.34 - ETA: 0s - loss: 10.0157 - acc: 0.34 - ETA: 0s - loss: 10.0291 - acc: 0.34 - ETA: 0s - loss: 10.0263 - acc: 0.34 - ETA: 0s - loss: 10.0232 - acc: 0.34 - ETA: 0s - loss: 10.0051 - acc: 0.34 - ETA: 0s - loss: 10.0114 - acc: 0.34 - ETA: 0s - loss: 9.9832 - acc: 0.3512 - ETA: 0s - loss: 9.9762 - acc: 0.351 - ETA: 0s - loss: 9.9374 - acc: 0.354 - ETA: 0s - loss: 9.9274 - acc: 0.354 - ETA: 0s - loss: 9.9274 - acc: 0.354 - ETA: 0s - loss: 9.9125 - acc: 0.355 - ETA: 0s - loss: 9.9275 - acc: 0.354 - ETA: 0s - loss: 9.9327 - acc: 0.354 - ETA: 0s - loss: 9.9397 - acc: 0.353 - ETA: 0s - loss: 9.9325 - acc: 0.354 - ETA: 0s - loss: 9.9202 - acc: 0.355 - ETA: 0s - loss: 9.8895 - acc: 0.356 - ETA: 0s - loss: 9.8693 - acc: 0.358 - ETA: 0s - loss: 9.8605 - acc: 0.3592Epoch 00004: val_loss improved from 10.32665 to 10.20823, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.8654 - acc: 0.3590 - val_loss: 10.2082 - val_acc: 0.3042 Epoch 6/20 6640/6680 [============================>.] - ETA: 2s - loss: 10.9701 - acc: 0.30 - ETA: 2s - loss: 9.8091 - acc: 0.3786 - ETA: 2s - loss: 10.1881 - acc: 0.36 - ETA: 2s - loss: 9.7420 - acc: 0.3850 - ETA: 2s - loss: 9.6542 - acc: 0.392 - ETA: 2s - loss: 9.9455 - acc: 0.374 - ETA: 2s - loss: 9.8391 - acc: 0.376 - ETA: 2s - loss: 9.7711 - acc: 0.378 - ETA: 2s - loss: 9.7488 - acc: 0.379 - ETA: 2s - loss: 9.7075 - acc: 0.382 - ETA: 2s - loss: 9.6810 - acc: 0.383 - ETA: 2s - loss: 9.7469 - acc: 0.380 - ETA: 2s - loss: 9.7940 - acc: 0.376 - ETA: 1s - loss: 9.7689 - acc: 0.376 - ETA: 1s - loss: 9.6715 - acc: 0.382 - ETA: 1s - loss: 9.5971 - acc: 0.386 - ETA: 1s - loss: 9.6284 - acc: 0.384 - ETA: 1s - loss: 9.6155 - acc: 0.385 - ETA: 1s - loss: 9.6447 - acc: 0.382 - ETA: 1s - loss: 9.6331 - acc: 0.382 - ETA: 1s - loss: 9.6495 - acc: 0.380 - ETA: 1s - loss: 9.6267 - acc: 0.381 - ETA: 1s - loss: 9.5865 - acc: 0.383 - ETA: 1s - loss: 9.5767 - acc: 0.383 - ETA: 1s - loss: 9.5811 - acc: 0.383 - ETA: 1s - loss: 9.6101 - acc: 0.381 - ETA: 1s - loss: 9.5678 - acc: 0.384 - ETA: 1s - loss: 9.5979 - acc: 0.382 - ETA: 1s - loss: 9.6321 - acc: 0.380 - ETA: 1s - loss: 9.6474 - acc: 0.379 - ETA: 1s - loss: 9.6397 - acc: 0.380 - ETA: 1s - loss: 9.6530 - acc: 0.379 - ETA: 1s - loss: 9.6386 - acc: 0.380 - ETA: 0s - loss: 9.6489 - acc: 0.380 - ETA: 0s - loss: 9.6593 - acc: 0.379 - ETA: 0s - loss: 9.6172 - acc: 0.381 - ETA: 0s - loss: 9.6041 - acc: 0.382 - ETA: 0s - loss: 9.5860 - acc: 0.383 - ETA: 0s - loss: 9.6095 - acc: 0.382 - ETA: 0s - loss: 9.6401 - acc: 0.380 - ETA: 0s - loss: 9.6505 - acc: 0.379 - ETA: 0s - loss: 9.6641 - acc: 0.378 - ETA: 0s - loss: 9.6469 - acc: 0.378 - ETA: 0s - loss: 9.6461 - acc: 0.378 - ETA: 0s - loss: 9.6288 - acc: 0.379 - ETA: 0s - loss: 9.6242 - acc: 0.379 - ETA: 0s - loss: 9.6759 - acc: 0.376 - ETA: 0s - loss: 9.6791 - acc: 0.375 - ETA: 0s - loss: 9.6825 - acc: 0.374 - ETA: 0s - loss: 9.6985 - acc: 0.373 - ETA: 0s - loss: 9.6992 - acc: 0.373 - ETA: 0s - loss: 9.7090 - acc: 0.3733Epoch 00005: val_loss improved from 10.20823 to 10.03545, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.7072 - acc: 0.3734 - val_loss: 10.0355 - val_acc: 0.3234 Epoch 7/20 6660/6680 [============================>.] - ETA: 3s - loss: 10.5075 - acc: 0.35 - ETA: 2s - loss: 10.4736 - acc: 0.32 - ETA: 2s - loss: 9.9836 - acc: 0.3643 - ETA: 2s - loss: 9.7948 - acc: 0.375 - ETA: 2s - loss: 10.0792 - acc: 0.35 - ETA: 2s - loss: 10.1472 - acc: 0.35 - ETA: 2s - loss: 9.9477 - acc: 0.3658 - ETA: 2s - loss: 9.8925 - acc: 0.371 - ETA: 2s - loss: 9.7173 - acc: 0.379 - ETA: 2s - loss: 9.7214 - acc: 0.378 - ETA: 2s - loss: 9.6294 - acc: 0.385 - ETA: 2s - loss: 9.6064 - acc: 0.387 - ETA: 2s - loss: 9.6316 - acc: 0.385 - ETA: 2s - loss: 9.5069 - acc: 0.393 - ETA: 2s - loss: 9.5047 - acc: 0.393 - ETA: 1s - loss: 9.4866 - acc: 0.393 - ETA: 1s - loss: 9.4871 - acc: 0.394 - ETA: 1s - loss: 9.5928 - acc: 0.388 - ETA: 1s - loss: 9.6427 - acc: 0.385 - ETA: 1s - loss: 9.6442 - acc: 0.386 - ETA: 1s - loss: 9.6597 - acc: 0.385 - ETA: 1s - loss: 9.6588 - acc: 0.385 - ETA: 1s - loss: 9.6379 - acc: 0.386 - ETA: 1s - loss: 9.6464 - acc: 0.386 - ETA: 1s - loss: 9.6233 - acc: 0.387 - ETA: 1s - loss: 9.6368 - acc: 0.386 - ETA: 1s - loss: 9.6312 - acc: 0.386 - ETA: 1s - loss: 9.6192 - acc: 0.388 - ETA: 1s - loss: 9.6236 - acc: 0.388 - ETA: 1s - loss: 9.6222 - acc: 0.387 - ETA: 1s - loss: 9.6350 - acc: 0.386 - ETA: 1s - loss: 9.6447 - acc: 0.385 - ETA: 1s - loss: 9.6391 - acc: 0.386 - ETA: 0s - loss: 9.6494 - acc: 0.385 - ETA: 0s - loss: 9.6380 - acc: 0.386 - ETA: 0s - loss: 9.5818 - acc: 0.388 - ETA: 0s - loss: 9.5680 - acc: 0.389 - ETA: 0s - loss: 9.5945 - acc: 0.387 - ETA: 0s - loss: 9.6148 - acc: 0.385 - ETA: 0s - loss: 9.6121 - acc: 0.385 - ETA: 0s - loss: 9.5997 - acc: 0.386 - ETA: 0s - loss: 9.5979 - acc: 0.386 - ETA: 0s - loss: 9.5925 - acc: 0.387 - ETA: 0s - loss: 9.5731 - acc: 0.388 - ETA: 0s - loss: 9.5922 - acc: 0.387 - ETA: 0s - loss: 9.5877 - acc: 0.387 - ETA: 0s - loss: 9.5940 - acc: 0.386 - ETA: 0s - loss: 9.5788 - acc: 0.387 - ETA: 0s - loss: 9.5968 - acc: 0.385 - ETA: 0s - loss: 9.5939 - acc: 0.386 - ETA: 0s - loss: 9.5835 - acc: 0.3866Epoch 00006: val_loss improved from 10.03545 to 9.91805, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.5765 - acc: 0.3871 - val_loss: 9.9180 - val_acc: 0.3210 Epoch 8/20 6560/6680 [============================>.] - ETA: 2s - loss: 9.4152 - acc: 0.400 - ETA: 2s - loss: 9.0724 - acc: 0.431 - ETA: 2s - loss: 8.8047 - acc: 0.440 - ETA: 2s - loss: 8.7082 - acc: 0.443 - ETA: 2s - loss: 9.0810 - acc: 0.419 - ETA: 2s - loss: 8.9980 - acc: 0.426 - ETA: 2s - loss: 9.2344 - acc: 0.412 - ETA: 2s - loss: 9.1880 - acc: 0.415 - ETA: 2s - loss: 9.1887 - acc: 0.414 - ETA: 2s - loss: 9.2491 - acc: 0.411 - ETA: 2s - loss: 9.2041 - acc: 0.414 - ETA: 2s - loss: 9.1584 - acc: 0.416 - ETA: 1s - loss: 9.1924 - acc: 0.415 - ETA: 1s - loss: 9.3390 - acc: 0.406 - ETA: 1s - loss: 9.3643 - acc: 0.404 - ETA: 1s - loss: 9.3598 - acc: 0.404 - ETA: 1s - loss: 9.3807 - acc: 0.404 - ETA: 1s - loss: 9.4231 - acc: 0.401 - ETA: 1s - loss: 9.4436 - acc: 0.401 - ETA: 1s - loss: 9.4183 - acc: 0.402 - ETA: 1s - loss: 9.3983 - acc: 0.403 - ETA: 1s - loss: 9.4137 - acc: 0.402 - ETA: 1s - loss: 9.3996 - acc: 0.403 - ETA: 1s - loss: 9.4248 - acc: 0.401 - ETA: 1s - loss: 9.4335 - acc: 0.400 - ETA: 1s - loss: 9.4800 - acc: 0.397 - ETA: 1s - loss: 9.4490 - acc: 0.397 - ETA: 1s - loss: 9.4580 - acc: 0.397 - ETA: 1s - loss: 9.4939 - acc: 0.394 - ETA: 0s - loss: 9.4446 - acc: 0.397 - ETA: 0s - loss: 9.4322 - acc: 0.397 - ETA: 0s - loss: 9.4445 - acc: 0.397 - ETA: 0s - loss: 9.4463 - acc: 0.396 - ETA: 0s - loss: 9.4635 - acc: 0.395 - ETA: 0s - loss: 9.4537 - acc: 0.396 - ETA: 0s - loss: 9.4559 - acc: 0.395 - ETA: 0s - loss: 9.4908 - acc: 0.393 - ETA: 0s - loss: 9.4745 - acc: 0.394 - ETA: 0s - loss: 9.4561 - acc: 0.395 - ETA: 0s - loss: 9.4622 - acc: 0.394 - ETA: 0s - loss: 9.4538 - acc: 0.395 - ETA: 0s - loss: 9.4245 - acc: 0.396 - ETA: 0s - loss: 9.4295 - acc: 0.396 - ETA: 0s - loss: 9.4215 - acc: 0.397 - ETA: 0s - loss: 9.4251 - acc: 0.397 - ETA: 0s - loss: 9.4175 - acc: 0.397 - ETA: 0s - loss: 9.4347 - acc: 0.396 - ETA: 0s - loss: 9.4256 - acc: 0.3974Epoch 00007: val_loss improved from 9.91805 to 9.72469, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.4347 - acc: 0.3964 - val_loss: 9.7247 - val_acc: 0.3389 Epoch 9/20 6540/6680 [============================>.] - ETA: 2s - loss: 8.0594 - acc: 0.500 - ETA: 2s - loss: 8.5524 - acc: 0.461 - ETA: 2s - loss: 8.8934 - acc: 0.434 - ETA: 2s - loss: 8.3089 - acc: 0.467 - ETA: 2s - loss: 8.5559 - acc: 0.451 - ETA: 2s - loss: 8.9177 - acc: 0.430 - ETA: 2s - loss: 9.0043 - acc: 0.423 - ETA: 2s - loss: 9.1596 - acc: 0.414 - ETA: 2s - loss: 9.2709 - acc: 0.409 - ETA: 2s - loss: 9.2432 - acc: 0.412 - ETA: 2s - loss: 9.1091 - acc: 0.421 - ETA: 2s - loss: 9.1195 - acc: 0.421 - ETA: 2s - loss: 9.1766 - acc: 0.418 - ETA: 1s - loss: 9.1239 - acc: 0.422 - ETA: 1s - loss: 9.1722 - acc: 0.417 - ETA: 1s - loss: 9.2398 - acc: 0.412 - ETA: 1s - loss: 9.2264 - acc: 0.412 - ETA: 1s - loss: 9.2039 - acc: 0.414 - ETA: 1s - loss: 9.2546 - acc: 0.410 - ETA: 1s - loss: 9.2586 - acc: 0.410 - ETA: 1s - loss: 9.2995 - acc: 0.407 - ETA: 1s - loss: 9.3223 - acc: 0.406 - ETA: 1s - loss: 9.3523 - acc: 0.404 - ETA: 1s - loss: 9.3276 - acc: 0.405 - ETA: 1s - loss: 9.3597 - acc: 0.403 - ETA: 1s - loss: 9.3214 - acc: 0.404 - ETA: 1s - loss: 9.3242 - acc: 0.405 - ETA: 1s - loss: 9.2916 - acc: 0.407 - ETA: 1s - loss: 9.3043 - acc: 0.406 - ETA: 1s - loss: 9.3454 - acc: 0.404 - ETA: 1s - loss: 9.3541 - acc: 0.403 - ETA: 0s - loss: 9.3387 - acc: 0.405 - ETA: 0s - loss: 9.3077 - acc: 0.407 - ETA: 0s - loss: 9.3264 - acc: 0.406 - ETA: 0s - loss: 9.3293 - acc: 0.406 - ETA: 0s - loss: 9.3245 - acc: 0.405 - ETA: 0s - loss: 9.2842 - acc: 0.408 - ETA: 0s - loss: 9.2954 - acc: 0.407 - ETA: 0s - loss: 9.3163 - acc: 0.406 - ETA: 0s - loss: 9.3398 - acc: 0.404 - ETA: 0s - loss: 9.3564 - acc: 0.404 - ETA: 0s - loss: 9.3564 - acc: 0.404 - ETA: 0s - loss: 9.3429 - acc: 0.405 - ETA: 0s - loss: 9.3394 - acc: 0.405 - ETA: 0s - loss: 9.3282 - acc: 0.406 - ETA: 0s - loss: 9.3236 - acc: 0.406 - ETA: 0s - loss: 9.3162 - acc: 0.407 - ETA: 0s - loss: 9.3312 - acc: 0.406 - ETA: 0s - loss: 9.3274 - acc: 0.4064Epoch 00008: val_loss improved from 9.72469 to 9.66704, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.3102 - acc: 0.4075 - val_loss: 9.6670 - val_acc: 0.3425 Epoch 10/20 6600/6680 [============================>.] - ETA: 2s - loss: 5.6418 - acc: 0.650 - ETA: 2s - loss: 9.5585 - acc: 0.400 - ETA: 2s - loss: 9.1868 - acc: 0.423 - ETA: 2s - loss: 8.5381 - acc: 0.465 - ETA: 2s - loss: 8.6784 - acc: 0.455 - ETA: 2s - loss: 8.8026 - acc: 0.448 - ETA: 2s - loss: 8.7649 - acc: 0.450 - ETA: 2s - loss: 8.8900 - acc: 0.440 - ETA: 2s - loss: 8.8789 - acc: 0.438 - ETA: 2s - loss: 8.9551 - acc: 0.432 - ETA: 2s - loss: 9.1415 - acc: 0.420 - ETA: 2s - loss: 9.2987 - acc: 0.411 - ETA: 2s - loss: 9.3593 - acc: 0.408 - ETA: 1s - loss: 9.2710 - acc: 0.412 - ETA: 1s - loss: 9.2847 - acc: 0.412 - ETA: 1s - loss: 9.2627 - acc: 0.413 - ETA: 1s - loss: 9.2648 - acc: 0.413 - ETA: 1s - loss: 9.2840 - acc: 0.412 - ETA: 1s - loss: 9.3136 - acc: 0.410 - ETA: 1s - loss: 9.2660 - acc: 0.413 - ETA: 1s - loss: 9.2337 - acc: 0.416 - ETA: 1s - loss: 9.2961 - acc: 0.412 - ETA: 1s - loss: 9.2870 - acc: 0.413 - ETA: 1s - loss: 9.3066 - acc: 0.411 - ETA: 1s - loss: 9.2590 - acc: 0.415 - ETA: 1s - loss: 9.2964 - acc: 0.412 - ETA: 1s - loss: 9.2656 - acc: 0.413 - ETA: 1s - loss: 9.2752 - acc: 0.413 - ETA: 1s - loss: 9.2870 - acc: 0.412 - ETA: 1s - loss: 9.2820 - acc: 0.412 - ETA: 1s - loss: 9.2923 - acc: 0.411 - ETA: 0s - loss: 9.3378 - acc: 0.408 - ETA: 0s - loss: 9.3476 - acc: 0.408 - ETA: 0s - loss: 9.3730 - acc: 0.406 - ETA: 0s - loss: 9.3654 - acc: 0.406 - ETA: 0s - loss: 9.3546 - acc: 0.407 - ETA: 0s - loss: 9.3181 - acc: 0.409 - ETA: 0s - loss: 9.3354 - acc: 0.408 - ETA: 0s - loss: 9.3320 - acc: 0.408 - ETA: 0s - loss: 9.3351 - acc: 0.407 - ETA: 0s - loss: 9.3394 - acc: 0.407 - ETA: 0s - loss: 9.3350 - acc: 0.408 - ETA: 0s - loss: 9.3015 - acc: 0.410 - ETA: 0s - loss: 9.2849 - acc: 0.411 - ETA: 0s - loss: 9.2712 - acc: 0.412 - ETA: 0s - loss: 9.2729 - acc: 0.411 - ETA: 0s - loss: 9.2545 - acc: 0.412 - ETA: 0s - loss: 9.2663 - acc: 0.412 - ETA: 0s - loss: 9.2603 - acc: 0.412 - ETA: 0s - loss: 9.2499 - acc: 0.4133Epoch 00009: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 9.2286 - acc: 0.4148 - val_loss: 9.6755 - val_acc: 0.3341 Epoch 11/20 6580/6680 [============================>.] - ETA: 2s - loss: 9.6710 - acc: 0.400 - ETA: 2s - loss: 10.0741 - acc: 0.37 - ETA: 2s - loss: 9.0320 - acc: 0.4367 - ETA: 2s - loss: 8.9093 - acc: 0.445 - ETA: 2s - loss: 8.8709 - acc: 0.448 - ETA: 2s - loss: 9.1034 - acc: 0.432 - ETA: 2s - loss: 9.1065 - acc: 0.432 - ETA: 2s - loss: 9.0848 - acc: 0.431 - ETA: 2s - loss: 9.1339 - acc: 0.428 - ETA: 2s - loss: 9.1713 - acc: 0.425 - ETA: 2s - loss: 9.2044 - acc: 0.423 - ETA: 2s - loss: 9.0692 - acc: 0.431 - ETA: 1s - loss: 9.0912 - acc: 0.430 - ETA: 1s - loss: 9.0946 - acc: 0.430 - ETA: 1s - loss: 9.1751 - acc: 0.425 - ETA: 1s - loss: 9.1574 - acc: 0.426 - ETA: 1s - loss: 9.1688 - acc: 0.425 - ETA: 1s - loss: 9.1944 - acc: 0.423 - ETA: 1s - loss: 9.1704 - acc: 0.424 - ETA: 1s - loss: 9.1817 - acc: 0.423 - ETA: 1s - loss: 9.1986 - acc: 0.421 - ETA: 1s - loss: 9.1758 - acc: 0.422 - ETA: 1s - loss: 9.1377 - acc: 0.425 - ETA: 1s - loss: 9.1207 - acc: 0.426 - ETA: 1s - loss: 9.1355 - acc: 0.425 - ETA: 1s - loss: 9.1070 - acc: 0.426 - ETA: 1s - loss: 9.0970 - acc: 0.426 - ETA: 1s - loss: 9.1214 - acc: 0.425 - ETA: 1s - loss: 9.1266 - acc: 0.424 - ETA: 1s - loss: 9.1540 - acc: 0.423 - ETA: 1s - loss: 9.1406 - acc: 0.424 - ETA: 0s - loss: 9.1590 - acc: 0.423 - ETA: 0s - loss: 9.1757 - acc: 0.422 - ETA: 0s - loss: 9.1877 - acc: 0.421 - ETA: 0s - loss: 9.1955 - acc: 0.421 - ETA: 0s - loss: 9.2237 - acc: 0.418 - ETA: 0s - loss: 9.2194 - acc: 0.418 - ETA: 0s - loss: 9.2319 - acc: 0.418 - ETA: 0s - loss: 9.2238 - acc: 0.418 - ETA: 0s - loss: 9.2267 - acc: 0.418 - ETA: 0s - loss: 9.2206 - acc: 0.419 - ETA: 0s - loss: 9.1870 - acc: 0.421 - ETA: 0s - loss: 9.1852 - acc: 0.421 - ETA: 0s - loss: 9.1830 - acc: 0.421 - ETA: 0s - loss: 9.1695 - acc: 0.422 - ETA: 0s - loss: 9.1646 - acc: 0.422 - ETA: 0s - loss: 9.1491 - acc: 0.423 - ETA: 0s - loss: 9.1574 - acc: 0.423 - ETA: 0s - loss: 9.1914 - acc: 0.4214Epoch 00010: val_loss improved from 9.66704 to 9.66146, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.1844 - acc: 0.4219 - val_loss: 9.6615 - val_acc: 0.3389 Epoch 12/20 6560/6680 [============================>.] - ETA: 2s - loss: 9.6719 - acc: 0.400 - ETA: 2s - loss: 9.0673 - acc: 0.437 - ETA: 2s - loss: 9.3499 - acc: 0.420 - ETA: 2s - loss: 9.4312 - acc: 0.411 - ETA: 2s - loss: 9.6050 - acc: 0.398 - ETA: 2s - loss: 9.5913 - acc: 0.400 - ETA: 2s - loss: 9.6256 - acc: 0.398 - ETA: 2s - loss: 9.6482 - acc: 0.397 - ETA: 2s - loss: 9.5410 - acc: 0.404 - ETA: 2s - loss: 9.4749 - acc: 0.408 - ETA: 2s - loss: 9.3953 - acc: 0.413 - ETA: 2s - loss: 9.2965 - acc: 0.419 - ETA: 2s - loss: 9.2647 - acc: 0.421 - ETA: 2s - loss: 9.2743 - acc: 0.420 - ETA: 2s - loss: 9.3008 - acc: 0.419 - ETA: 1s - loss: 9.2327 - acc: 0.421 - ETA: 1s - loss: 9.2713 - acc: 0.419 - ETA: 1s - loss: 9.2381 - acc: 0.421 - ETA: 1s - loss: 9.1819 - acc: 0.425 - ETA: 1s - loss: 9.1900 - acc: 0.425 - ETA: 1s - loss: 9.1791 - acc: 0.426 - ETA: 1s - loss: 9.1635 - acc: 0.427 - ETA: 1s - loss: 9.2055 - acc: 0.424 - ETA: 1s - loss: 9.2492 - acc: 0.421 - ETA: 1s - loss: 9.2231 - acc: 0.423 - ETA: 1s - loss: 9.2513 - acc: 0.420 - ETA: 1s - loss: 9.2218 - acc: 0.423 - ETA: 1s - loss: 9.2444 - acc: 0.421 - ETA: 1s - loss: 9.2524 - acc: 0.420 - ETA: 1s - loss: 9.2517 - acc: 0.420 - ETA: 1s - loss: 9.2322 - acc: 0.421 - ETA: 1s - loss: 9.2086 - acc: 0.423 - ETA: 0s - loss: 9.2212 - acc: 0.422 - ETA: 0s - loss: 9.2235 - acc: 0.422 - ETA: 0s - loss: 9.2312 - acc: 0.421 - ETA: 0s - loss: 9.2356 - acc: 0.421 - ETA: 0s - loss: 9.2058 - acc: 0.423 - ETA: 0s - loss: 9.2146 - acc: 0.422 - ETA: 0s - loss: 9.2015 - acc: 0.423 - ETA: 0s - loss: 9.1864 - acc: 0.424 - ETA: 0s - loss: 9.1877 - acc: 0.424 - ETA: 0s - loss: 9.1989 - acc: 0.423 - ETA: 0s - loss: 9.2107 - acc: 0.422 - ETA: 0s - loss: 9.2015 - acc: 0.422 - ETA: 0s - loss: 9.1827 - acc: 0.423 - ETA: 0s - loss: 9.1605 - acc: 0.425 - ETA: 0s - loss: 9.1544 - acc: 0.425 - ETA: 0s - loss: 9.1692 - acc: 0.424 - ETA: 0s - loss: 9.1654 - acc: 0.424 - ETA: 0s - loss: 9.1606 - acc: 0.4245Epoch 00011: val_loss improved from 9.66146 to 9.62308, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.1555 - acc: 0.4247 - val_loss: 9.6231 - val_acc: 0.3377 Epoch 13/20 6620/6680 [============================>.] - ETA: 2s - loss: 5.4514 - acc: 0.550 - ETA: 2s - loss: 8.2635 - acc: 0.471 - ETA: 2s - loss: 8.8557 - acc: 0.439 - ETA: 2s - loss: 9.1204 - acc: 0.426 - ETA: 2s - loss: 9.1200 - acc: 0.425 - ETA: 2s - loss: 8.9359 - acc: 0.437 - ETA: 2s - loss: 9.0616 - acc: 0.429 - ETA: 2s - loss: 9.1547 - acc: 0.425 - ETA: 2s - loss: 9.1073 - acc: 0.425 - ETA: 2s - loss: 9.3087 - acc: 0.413 - ETA: 2s - loss: 9.2534 - acc: 0.416 - ETA: 2s - loss: 9.1086 - acc: 0.426 - ETA: 1s - loss: 9.2123 - acc: 0.420 - ETA: 1s - loss: 9.2447 - acc: 0.416 - ETA: 1s - loss: 9.2784 - acc: 0.414 - ETA: 1s - loss: 9.3014 - acc: 0.411 - ETA: 1s - loss: 9.2512 - acc: 0.414 - ETA: 1s - loss: 9.3126 - acc: 0.411 - ETA: 1s - loss: 9.2987 - acc: 0.412 - ETA: 1s - loss: 9.2821 - acc: 0.413 - ETA: 1s - loss: 9.1804 - acc: 0.419 - ETA: 1s - loss: 9.1547 - acc: 0.421 - ETA: 1s - loss: 9.1844 - acc: 0.419 - ETA: 1s - loss: 9.1934 - acc: 0.418 - ETA: 1s - loss: 9.1915 - acc: 0.419 - ETA: 1s - loss: 9.2184 - acc: 0.417 - ETA: 1s - loss: 9.1756 - acc: 0.420 - ETA: 1s - loss: 9.1200 - acc: 0.423 - ETA: 1s - loss: 9.1002 - acc: 0.424 - ETA: 1s - loss: 9.0990 - acc: 0.425 - ETA: 1s - loss: 9.0935 - acc: 0.425 - ETA: 0s - loss: 9.0734 - acc: 0.426 - ETA: 0s - loss: 9.1172 - acc: 0.424 - ETA: 0s - loss: 9.0959 - acc: 0.425 - ETA: 0s - loss: 9.0838 - acc: 0.426 - ETA: 0s - loss: 9.1010 - acc: 0.424 - ETA: 0s - loss: 9.1153 - acc: 0.423 - ETA: 0s - loss: 9.0963 - acc: 0.424 - ETA: 0s - loss: 9.0974 - acc: 0.424 - ETA: 0s - loss: 9.0945 - acc: 0.425 - ETA: 0s - loss: 9.0479 - acc: 0.428 - ETA: 0s - loss: 9.0674 - acc: 0.427 - ETA: 0s - loss: 9.0626 - acc: 0.427 - ETA: 0s - loss: 9.0451 - acc: 0.428 - ETA: 0s - loss: 9.0479 - acc: 0.428 - ETA: 0s - loss: 9.0393 - acc: 0.428 - ETA: 0s - loss: 9.0254 - acc: 0.429 - ETA: 0s - loss: 9.0231 - acc: 0.429 - ETA: 0s - loss: 9.0153 - acc: 0.430 - ETA: 0s - loss: 9.0098 - acc: 0.4310Epoch 00012: val_loss improved from 9.62308 to 9.57260, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 9.0063 - acc: 0.4311 - val_loss: 9.5726 - val_acc: 0.3473 Epoch 14/20 6640/6680 [============================>.] - ETA: 2s - loss: 9.9510 - acc: 0.350 - ETA: 2s - loss: 8.4489 - acc: 0.471 - ETA: 2s - loss: 9.0017 - acc: 0.435 - ETA: 2s - loss: 8.9539 - acc: 0.440 - ETA: 2s - loss: 8.8528 - acc: 0.446 - ETA: 2s - loss: 8.8818 - acc: 0.444 - ETA: 2s - loss: 8.7916 - acc: 0.447 - ETA: 2s - loss: 8.7247 - acc: 0.452 - ETA: 2s - loss: 8.8037 - acc: 0.445 - ETA: 2s - loss: 8.8357 - acc: 0.444 - ETA: 1s - loss: 8.8536 - acc: 0.443 - ETA: 1s - loss: 8.8685 - acc: 0.442 - ETA: 1s - loss: 8.8750 - acc: 0.441 - ETA: 1s - loss: 8.7886 - acc: 0.445 - ETA: 1s - loss: 8.8128 - acc: 0.443 - ETA: 1s - loss: 8.8161 - acc: 0.444 - ETA: 1s - loss: 8.8545 - acc: 0.441 - ETA: 1s - loss: 8.8369 - acc: 0.442 - ETA: 1s - loss: 8.8885 - acc: 0.439 - ETA: 1s - loss: 8.9108 - acc: 0.438 - ETA: 1s - loss: 8.9383 - acc: 0.436 - ETA: 1s - loss: 8.9317 - acc: 0.437 - ETA: 1s - loss: 8.9091 - acc: 0.438 - ETA: 1s - loss: 8.9234 - acc: 0.437 - ETA: 1s - loss: 8.9935 - acc: 0.433 - ETA: 1s - loss: 9.0134 - acc: 0.432 - ETA: 1s - loss: 8.9861 - acc: 0.434 - ETA: 1s - loss: 8.9991 - acc: 0.433 - ETA: 1s - loss: 9.0489 - acc: 0.430 - ETA: 1s - loss: 9.0142 - acc: 0.432 - ETA: 0s - loss: 9.0397 - acc: 0.431 - ETA: 0s - loss: 9.0150 - acc: 0.432 - ETA: 0s - loss: 8.9822 - acc: 0.434 - ETA: 0s - loss: 8.9592 - acc: 0.435 - ETA: 0s - loss: 8.9566 - acc: 0.436 - ETA: 0s - loss: 8.9631 - acc: 0.435 - ETA: 0s - loss: 8.9452 - acc: 0.436 - ETA: 0s - loss: 8.9176 - acc: 0.438 - ETA: 0s - loss: 8.9022 - acc: 0.439 - ETA: 0s - loss: 8.8992 - acc: 0.439 - ETA: 0s - loss: 8.9105 - acc: 0.438 - ETA: 0s - loss: 8.8899 - acc: 0.440 - ETA: 0s - loss: 8.8800 - acc: 0.440 - ETA: 0s - loss: 8.8931 - acc: 0.439 - ETA: 0s - loss: 8.8623 - acc: 0.440 - ETA: 0s - loss: 8.8166 - acc: 0.443 - ETA: 0s - loss: 8.8218 - acc: 0.443 - ETA: 0s - loss: 8.8329 - acc: 0.442 - ETA: 0s - loss: 8.8335 - acc: 0.441 - ETA: 0s - loss: 8.8826 - acc: 0.4390Epoch 00013: val_loss improved from 9.57260 to 9.29336, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.8810 - acc: 0.4389 - val_loss: 9.2934 - val_acc: 0.3497 Epoch 15/20 6580/6680 [============================>.] - ETA: 2s - loss: 8.8650 - acc: 0.450 - ETA: 2s - loss: 8.7261 - acc: 0.450 - ETA: 2s - loss: 8.6497 - acc: 0.450 - ETA: 2s - loss: 8.6985 - acc: 0.450 - ETA: 2s - loss: 8.7614 - acc: 0.448 - ETA: 2s - loss: 8.8775 - acc: 0.441 - ETA: 2s - loss: 8.8699 - acc: 0.439 - ETA: 2s - loss: 8.8876 - acc: 0.439 - ETA: 2s - loss: 8.8850 - acc: 0.437 - ETA: 2s - loss: 8.7757 - acc: 0.444 - ETA: 2s - loss: 8.8188 - acc: 0.442 - ETA: 1s - loss: 8.8207 - acc: 0.442 - ETA: 1s - loss: 8.8901 - acc: 0.438 - ETA: 1s - loss: 8.8161 - acc: 0.443 - ETA: 1s - loss: 8.7873 - acc: 0.445 - ETA: 1s - loss: 8.7470 - acc: 0.447 - ETA: 1s - loss: 8.7093 - acc: 0.450 - ETA: 1s - loss: 8.7828 - acc: 0.445 - ETA: 1s - loss: 8.8549 - acc: 0.440 - ETA: 1s - loss: 8.8405 - acc: 0.441 - ETA: 1s - loss: 8.8059 - acc: 0.443 - ETA: 1s - loss: 8.8108 - acc: 0.443 - ETA: 1s - loss: 8.7875 - acc: 0.445 - ETA: 1s - loss: 8.7981 - acc: 0.444 - ETA: 1s - loss: 8.7963 - acc: 0.443 - ETA: 1s - loss: 8.7261 - acc: 0.448 - ETA: 1s - loss: 8.7465 - acc: 0.447 - ETA: 1s - loss: 8.7343 - acc: 0.448 - ETA: 1s - loss: 8.7226 - acc: 0.448 - ETA: 1s - loss: 8.7295 - acc: 0.448 - ETA: 1s - loss: 8.7530 - acc: 0.447 - ETA: 0s - loss: 8.7595 - acc: 0.446 - ETA: 0s - loss: 8.7566 - acc: 0.447 - ETA: 0s - loss: 8.7327 - acc: 0.448 - ETA: 0s - loss: 8.7341 - acc: 0.448 - ETA: 0s - loss: 8.7092 - acc: 0.449 - ETA: 0s - loss: 8.7073 - acc: 0.448 - ETA: 0s - loss: 8.7041 - acc: 0.449 - ETA: 0s - loss: 8.7244 - acc: 0.448 - ETA: 0s - loss: 8.7294 - acc: 0.447 - ETA: 0s - loss: 8.7190 - acc: 0.448 - ETA: 0s - loss: 8.7085 - acc: 0.449 - ETA: 0s - loss: 8.7195 - acc: 0.448 - ETA: 0s - loss: 8.7318 - acc: 0.448 - ETA: 0s - loss: 8.7426 - acc: 0.447 - ETA: 0s - loss: 8.7570 - acc: 0.446 - ETA: 0s - loss: 8.7360 - acc: 0.448 - ETA: 0s - loss: 8.7163 - acc: 0.449 - ETA: 0s - loss: 8.7171 - acc: 0.449 - ETA: 0s - loss: 8.7037 - acc: 0.4506Epoch 00014: val_loss improved from 9.29336 to 9.21769, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.7128 - acc: 0.4500 - val_loss: 9.2177 - val_acc: 0.3725 Epoch 16/20 6580/6680 [============================>.] - ETA: 3s - loss: 8.8655 - acc: 0.450 - ETA: 2s - loss: 8.5636 - acc: 0.468 - ETA: 2s - loss: 8.5822 - acc: 0.463 - ETA: 2s - loss: 8.8281 - acc: 0.445 - ETA: 2s - loss: 8.8117 - acc: 0.446 - ETA: 2s - loss: 8.7724 - acc: 0.450 - ETA: 2s - loss: 8.7309 - acc: 0.452 - ETA: 2s - loss: 8.7373 - acc: 0.452 - ETA: 2s - loss: 8.6598 - acc: 0.457 - ETA: 2s - loss: 8.7895 - acc: 0.449 - ETA: 2s - loss: 8.7617 - acc: 0.451 - ETA: 2s - loss: 8.6566 - acc: 0.458 - ETA: 2s - loss: 8.7949 - acc: 0.450 - ETA: 2s - loss: 8.8073 - acc: 0.449 - ETA: 1s - loss: 8.8131 - acc: 0.448 - ETA: 1s - loss: 8.8171 - acc: 0.448 - ETA: 1s - loss: 8.7757 - acc: 0.450 - ETA: 1s - loss: 8.8058 - acc: 0.447 - ETA: 1s - loss: 8.7598 - acc: 0.450 - ETA: 1s - loss: 8.7531 - acc: 0.450 - ETA: 1s - loss: 8.7258 - acc: 0.451 - ETA: 1s - loss: 8.7591 - acc: 0.449 - ETA: 1s - loss: 8.7708 - acc: 0.449 - ETA: 1s - loss: 8.7430 - acc: 0.451 - ETA: 1s - loss: 8.7376 - acc: 0.451 - ETA: 1s - loss: 8.7343 - acc: 0.451 - ETA: 1s - loss: 8.7020 - acc: 0.453 - ETA: 1s - loss: 8.6610 - acc: 0.455 - ETA: 1s - loss: 8.6808 - acc: 0.454 - ETA: 1s - loss: 8.7127 - acc: 0.452 - ETA: 1s - loss: 8.7074 - acc: 0.452 - ETA: 1s - loss: 8.7259 - acc: 0.451 - ETA: 0s - loss: 8.7189 - acc: 0.451 - ETA: 0s - loss: 8.6914 - acc: 0.453 - ETA: 0s - loss: 8.6754 - acc: 0.454 - ETA: 0s - loss: 8.6592 - acc: 0.455 - ETA: 0s - loss: 8.6820 - acc: 0.453 - ETA: 0s - loss: 8.6774 - acc: 0.453 - ETA: 0s - loss: 8.6704 - acc: 0.453 - ETA: 0s - loss: 8.6599 - acc: 0.454 - ETA: 0s - loss: 8.6419 - acc: 0.455 - ETA: 0s - loss: 8.6621 - acc: 0.454 - ETA: 0s - loss: 8.6776 - acc: 0.453 - ETA: 0s - loss: 8.6759 - acc: 0.453 - ETA: 0s - loss: 8.6694 - acc: 0.454 - ETA: 0s - loss: 8.7034 - acc: 0.451 - ETA: 0s - loss: 8.7093 - acc: 0.451 - ETA: 0s - loss: 8.7020 - acc: 0.451 - ETA: 0s - loss: 8.7006 - acc: 0.452 - ETA: 0s - loss: 8.6966 - acc: 0.452 - ETA: 0s - loss: 8.6751 - acc: 0.4536Epoch 00015: val_loss improved from 9.21769 to 9.10753, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.6614 - acc: 0.4543 - val_loss: 9.1075 - val_acc: 0.3677 Epoch 17/20 6640/6680 [============================>.] - ETA: 2s - loss: 8.8673 - acc: 0.450 - ETA: 2s - loss: 7.8752 - acc: 0.506 - ETA: 2s - loss: 8.5770 - acc: 0.456 - ETA: 2s - loss: 8.8889 - acc: 0.441 - ETA: 2s - loss: 8.7201 - acc: 0.451 - ETA: 2s - loss: 8.5997 - acc: 0.459 - ETA: 2s - loss: 8.7163 - acc: 0.452 - ETA: 2s - loss: 8.6276 - acc: 0.454 - ETA: 2s - loss: 8.5614 - acc: 0.459 - ETA: 2s - loss: 8.5697 - acc: 0.460 - ETA: 1s - loss: 8.5554 - acc: 0.461 - ETA: 1s - loss: 8.4731 - acc: 0.466 - ETA: 1s - loss: 8.4193 - acc: 0.470 - ETA: 1s - loss: 8.3996 - acc: 0.471 - ETA: 1s - loss: 8.4195 - acc: 0.469 - ETA: 1s - loss: 8.4267 - acc: 0.469 - ETA: 1s - loss: 8.4171 - acc: 0.468 - ETA: 1s - loss: 8.4458 - acc: 0.466 - ETA: 1s - loss: 8.4627 - acc: 0.466 - ETA: 1s - loss: 8.4771 - acc: 0.465 - ETA: 1s - loss: 8.4635 - acc: 0.466 - ETA: 1s - loss: 8.4469 - acc: 0.467 - ETA: 1s - loss: 8.4473 - acc: 0.467 - ETA: 1s - loss: 8.4807 - acc: 0.465 - ETA: 1s - loss: 8.5184 - acc: 0.463 - ETA: 1s - loss: 8.5477 - acc: 0.461 - ETA: 1s - loss: 8.5555 - acc: 0.461 - ETA: 1s - loss: 8.5724 - acc: 0.460 - ETA: 1s - loss: 8.5470 - acc: 0.462 - ETA: 1s - loss: 8.5369 - acc: 0.462 - ETA: 0s - loss: 8.5130 - acc: 0.464 - ETA: 0s - loss: 8.4998 - acc: 0.465 - ETA: 0s - loss: 8.4914 - acc: 0.466 - ETA: 0s - loss: 8.4881 - acc: 0.466 - ETA: 0s - loss: 8.5064 - acc: 0.465 - ETA: 0s - loss: 8.4844 - acc: 0.466 - ETA: 0s - loss: 8.5075 - acc: 0.465 - ETA: 0s - loss: 8.5252 - acc: 0.463 - ETA: 0s - loss: 8.5520 - acc: 0.462 - ETA: 0s - loss: 8.5758 - acc: 0.460 - ETA: 0s - loss: 8.5896 - acc: 0.459 - ETA: 0s - loss: 8.6247 - acc: 0.457 - ETA: 0s - loss: 8.5990 - acc: 0.459 - ETA: 0s - loss: 8.5744 - acc: 0.461 - ETA: 0s - loss: 8.5464 - acc: 0.462 - ETA: 0s - loss: 8.5750 - acc: 0.461 - ETA: 0s - loss: 8.5739 - acc: 0.461 - ETA: 0s - loss: 8.5656 - acc: 0.461 - ETA: 0s - loss: 8.5543 - acc: 0.462 - ETA: 0s - loss: 8.5644 - acc: 0.4617Epoch 00016: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 8.5614 - acc: 0.4620 - val_loss: 9.1235 - val_acc: 0.3701 Epoch 18/20 6520/6680 [============================>.] - ETA: 2s - loss: 7.2532 - acc: 0.550 - ETA: 2s - loss: 8.8772 - acc: 0.443 - ETA: 2s - loss: 8.5491 - acc: 0.466 - ETA: 2s - loss: 8.4522 - acc: 0.470 - ETA: 2s - loss: 8.4560 - acc: 0.467 - ETA: 2s - loss: 8.4006 - acc: 0.472 - ETA: 2s - loss: 8.3481 - acc: 0.476 - ETA: 2s - loss: 8.5047 - acc: 0.467 - ETA: 2s - loss: 8.5104 - acc: 0.465 - ETA: 2s - loss: 8.5384 - acc: 0.463 - ETA: 2s - loss: 8.4967 - acc: 0.466 - ETA: 2s - loss: 8.5390 - acc: 0.464 - ETA: 2s - loss: 8.5664 - acc: 0.462 - ETA: 1s - loss: 8.5443 - acc: 0.464 - ETA: 1s - loss: 8.5189 - acc: 0.466 - ETA: 1s - loss: 8.5590 - acc: 0.463 - ETA: 1s - loss: 8.5623 - acc: 0.463 - ETA: 1s - loss: 8.4890 - acc: 0.468 - ETA: 1s - loss: 8.5105 - acc: 0.466 - ETA: 1s - loss: 8.5121 - acc: 0.465 - ETA: 1s - loss: 8.4910 - acc: 0.466 - ETA: 1s - loss: 8.5061 - acc: 0.465 - ETA: 1s - loss: 8.4907 - acc: 0.466 - ETA: 1s - loss: 8.5534 - acc: 0.463 - ETA: 1s - loss: 8.5970 - acc: 0.460 - ETA: 1s - loss: 8.6368 - acc: 0.458 - ETA: 1s - loss: 8.6125 - acc: 0.459 - ETA: 1s - loss: 8.5961 - acc: 0.459 - ETA: 1s - loss: 8.6040 - acc: 0.459 - ETA: 1s - loss: 8.5610 - acc: 0.461 - ETA: 1s - loss: 8.5696 - acc: 0.461 - ETA: 1s - loss: 8.5721 - acc: 0.460 - ETA: 0s - loss: 8.6005 - acc: 0.459 - ETA: 0s - loss: 8.5803 - acc: 0.460 - ETA: 0s - loss: 8.5981 - acc: 0.458 - ETA: 0s - loss: 8.6170 - acc: 0.457 - ETA: 0s - loss: 8.5775 - acc: 0.459 - ETA: 0s - loss: 8.5684 - acc: 0.460 - ETA: 0s - loss: 8.5807 - acc: 0.459 - ETA: 0s - loss: 8.6053 - acc: 0.457 - ETA: 0s - loss: 8.5948 - acc: 0.458 - ETA: 0s - loss: 8.6077 - acc: 0.457 - ETA: 0s - loss: 8.5693 - acc: 0.460 - ETA: 0s - loss: 8.5536 - acc: 0.461 - ETA: 0s - loss: 8.5669 - acc: 0.460 - ETA: 0s - loss: 8.5833 - acc: 0.459 - ETA: 0s - loss: 8.5871 - acc: 0.458 - ETA: 0s - loss: 8.5815 - acc: 0.458 - ETA: 0s - loss: 8.5748 - acc: 0.458 - ETA: 0s - loss: 8.5699 - acc: 0.4583Epoch 00017: val_loss did not improve 6680/6680 [==============================] - 2s - loss: 8.5134 - acc: 0.4617 - val_loss: 9.1129 - val_acc: 0.3725 Epoch 19/20 6560/6680 [============================>.] - ETA: 2s - loss: 8.9206 - acc: 0.400 - ETA: 2s - loss: 8.8577 - acc: 0.431 - ETA: 2s - loss: 8.7971 - acc: 0.440 - ETA: 2s - loss: 8.6078 - acc: 0.452 - ETA: 2s - loss: 8.6118 - acc: 0.451 - ETA: 2s - loss: 8.5707 - acc: 0.455 - ETA: 2s - loss: 8.7137 - acc: 0.447 - ETA: 2s - loss: 8.5650 - acc: 0.456 - ETA: 2s - loss: 8.6334 - acc: 0.450 - ETA: 2s - loss: 8.6559 - acc: 0.449 - ETA: 2s - loss: 8.7518 - acc: 0.441 - ETA: 2s - loss: 8.7380 - acc: 0.442 - ETA: 2s - loss: 8.7404 - acc: 0.443 - ETA: 1s - loss: 8.7079 - acc: 0.444 - ETA: 1s - loss: 8.5852 - acc: 0.452 - ETA: 1s - loss: 8.5410 - acc: 0.455 - ETA: 1s - loss: 8.5642 - acc: 0.454 - ETA: 1s - loss: 8.6117 - acc: 0.451 - 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acc: 0.465 - ETA: 0s - loss: 8.4263 - acc: 0.464 - ETA: 0s - loss: 8.4191 - acc: 0.465 - ETA: 0s - loss: 8.4077 - acc: 0.466 - ETA: 0s - loss: 8.3918 - acc: 0.467 - ETA: 0s - loss: 8.3933 - acc: 0.4674Epoch 00018: val_loss improved from 9.10753 to 9.02554, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.3678 - acc: 0.4690 - val_loss: 9.0255 - val_acc: 0.3772 Epoch 20/20 6640/6680 [============================>.] - ETA: 2s - loss: 9.7238 - acc: 0.400 - ETA: 2s - loss: 8.6842 - acc: 0.456 - ETA: 2s - loss: 8.8884 - acc: 0.436 - ETA: 2s - loss: 9.1920 - acc: 0.419 - ETA: 2s - loss: 9.1391 - acc: 0.423 - ETA: 2s - loss: 8.8480 - acc: 0.442 - ETA: 2s - loss: 8.6789 - acc: 0.453 - ETA: 2s - loss: 8.6336 - acc: 0.457 - ETA: 2s - loss: 8.5208 - acc: 0.465 - ETA: 2s - loss: 8.5754 - acc: 0.462 - ETA: 2s - loss: 8.5362 - acc: 0.465 - ETA: 2s - loss: 8.5789 - acc: 0.463 - ETA: 2s - loss: 8.5851 - acc: 0.462 - ETA: 1s - loss: 8.5028 - 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ETA: 0s - loss: 8.2353 - acc: 0.482 - ETA: 0s - loss: 8.2158 - acc: 0.483 - ETA: 0s - loss: 8.2341 - acc: 0.482 - ETA: 0s - loss: 8.2418 - acc: 0.482 - ETA: 0s - loss: 8.2262 - acc: 0.483 - ETA: 0s - loss: 8.2340 - acc: 0.482 - ETA: 0s - loss: 8.2337 - acc: 0.482 - ETA: 0s - loss: 8.2572 - acc: 0.481 - ETA: 0s - loss: 8.2691 - acc: 0.480 - ETA: 0s - loss: 8.2775 - acc: 0.480 - ETA: 0s - loss: 8.2717 - acc: 0.480 - ETA: 0s - loss: 8.2868 - acc: 0.479 - ETA: 0s - loss: 8.3142 - acc: 0.4780Epoch 00019: val_loss improved from 9.02554 to 8.92000, saving model to saved_models/weights.best.VGG16.hdf5 6680/6680 [==============================] - 2s - loss: 8.3081 - acc: 0.4783 - val_loss: 8.9200 - val_acc: 0.3880
Out[60]:
<keras.callbacks.History at 0x1b45dfe4048>
Load the Model with the Best Validation Loss¶
In [61]:
VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')
Test the Model¶
Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.
In [62]:
# get index of predicted dog breed for each image in test set
VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]
# report test accuracy
test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 40.4306%
Predict Dog Breed with the Model¶
In [63]:
from extract_bottleneck_features import *
def VGG16_predict_breed(img_path):
# extract bottleneck features
bottleneck_feature = extract_VGG16(path_to_tensor(img_path))
# obtain predicted vector
predicted_vector = VGG16_model.predict(bottleneck_feature)
# return dog breed that is predicted by the model
return dog_names[np.argmax(predicted_vector)]
Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)¶
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:
- VGG-19 bottleneck features
- ResNet-50 bottleneck features
- Inception bottleneck features
- Xception bottleneck features
The files are encoded as such:
Dog{network}Data.npz
where {network}, in the above filename, can be one of VGG19, Resnet50, InceptionV3, or Xception. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the bottleneck_features/ folder in the repository.
(IMPLEMENTATION) Obtain Bottleneck Features¶
In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:
bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')
train_{network} = bottleneck_features['train']
valid_{network} = bottleneck_features['valid']
test_{network} = bottleneck_features['test']In [12]:
### TODO: Obtain bottleneck features from another pre-trained CNN.
bottleneck_features_Xception = np.load('bottleneck_features/DogXceptionData.npz')
train_Xception = bottleneck_features_Xception['train']
valid_Xception = bottleneck_features_Xception['valid']
test_Xception = bottleneck_features_Xception['test']
(IMPLEMENTATION) Model Architecture¶
Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:
<your model's name>.summary()
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer: After reading up on different vision models, I believe the architecture of Xception model allows it to achieve better accuracy compared to prior champions. I therefore decided to start from this version, remove final layer originally designed for classification of something else - and build up just a few levels on top instead for our task:
- Global averaging pooling layer would reduce input square of 7 by 7 into single neuron, fully condensing remaining spatial information from Xception model so the system would foget where on the original image the original pattern happened to be. This is because we only want to know presence of the dog, not her location. This also allows to reduce number of nodes by a factor of 49
- Final dense activation layer condenses all available information in a linear way into a classifier, which then transforms into probabilities summing up to one using softmax function
This network architecture should work much better due to transfer learning relying on pre-defined list of features that is already known to work on another wide-range classification problem - thus re-using prior solution to the problem of recognizing presence of local features and their relative position.
In [15]:
### TODO: Define your architecture.
print("The shape of the feature set is:", train_Xception.shape[0:])
transfer_learning_model = Sequential()
transfer_learning_model.add(GlobalAveragePooling2D(input_shape=train_Xception.shape[1:]))
transfer_learning_model.add(Dense(133, activation='softmax'))
transfer_learning_model.summary()
The shape of the feature set is: (6680, 7, 7, 2048) _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= global_average_pooling2d_1 ( (None, 2048) 0 _________________________________________________________________ dense_3 (Dense) (None, 133) 272517 ================================================================= Total params: 272,517 Trainable params: 272,517 Non-trainable params: 0 _________________________________________________________________
(IMPLEMENTATION) Compile the Model¶
In [16]:
### TODO: Compile the model.
transfer_learning_model.compile(
loss = 'categorical_crossentropy',
optimizer= 'Adadelta',
metrics = ['accuracy'])
(IMPLEMENTATION) Train the Model¶
Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.
You are welcome to augment the training data, but this is not a requirement.
In [27]:
### TODO: Train the model.
from keras.callbacks import ModelCheckpoint
checkpointer1 = ModelCheckpoint(filepath='saved_models/weights.best.Xception.hdf5',
verbose=1, save_best_only=True)
transfer_learning_model.fit(train_Xception, train_targets,
validation_data=(valid_Xception, valid_targets),
epochs=20, batch_size=50, callbacks=[checkpointer1], verbose=1)
Train on 6680 samples, validate on 835 samples Epoch 1/20 6650/6680 [============================>.] - ETA: 5s - loss: 0.2319 - acc: 0.900 - ETA: 5s - loss: 0.1714 - acc: 0.940 - ETA: 5s - loss: 0.1501 - acc: 0.955 - ETA: 5s - loss: 0.1575 - acc: 0.960 - ETA: 5s - loss: 0.1522 - acc: 0.965 - ETA: 6s - loss: 0.1447 - acc: 0.968 - ETA: 6s - loss: 0.1531 - acc: 0.964 - ETA: 6s - loss: 0.1559 - acc: 0.960 - ETA: 6s - loss: 0.1507 - acc: 0.961 - ETA: 6s - loss: 0.1558 - acc: 0.958 - ETA: 6s - loss: 0.1552 - acc: 0.960 - ETA: 6s - loss: 0.1535 - acc: 0.962 - ETA: 6s - loss: 0.1485 - acc: 0.965 - ETA: 6s - loss: 0.1478 - acc: 0.965 - ETA: 6s - loss: 0.1463 - acc: 0.965 - ETA: 6s - loss: 0.1476 - acc: 0.965 - ETA: 6s - loss: 0.1480 - acc: 0.964 - ETA: 6s - loss: 0.1469 - acc: 0.964 - ETA: 6s - loss: 0.1500 - acc: 0.962 - ETA: 6s - loss: 0.1586 - acc: 0.959 - ETA: 6s - loss: 0.1559 - acc: 0.960 - ETA: 6s - loss: 0.1536 - acc: 0.961 - ETA: 6s - loss: 0.1525 - acc: 0.960 - ETA: 6s - loss: 0.1515 - 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acc: 0.963 - ETA: 1s - loss: 0.1447 - acc: 0.963 - ETA: 1s - loss: 0.1441 - acc: 0.963 - ETA: 1s - loss: 0.1438 - acc: 0.963 - ETA: 1s - loss: 0.1449 - acc: 0.963 - ETA: 1s - loss: 0.1461 - acc: 0.962 - ETA: 1s - loss: 0.1460 - acc: 0.962 - ETA: 1s - loss: 0.1468 - acc: 0.962 - ETA: 0s - loss: 0.1466 - acc: 0.962 - ETA: 0s - loss: 0.1467 - acc: 0.962 - ETA: 0s - loss: 0.1471 - acc: 0.962 - ETA: 0s - loss: 0.1464 - acc: 0.962 - ETA: 0s - loss: 0.1465 - acc: 0.962 - ETA: 0s - loss: 0.1463 - acc: 0.962 - ETA: 0s - loss: 0.1471 - acc: 0.962 - ETA: 0s - loss: 0.1463 - acc: 0.962 - ETA: 0s - loss: 0.1463 - acc: 0.962 - ETA: 0s - loss: 0.1474 - acc: 0.962 - ETA: 0s - loss: 0.1477 - acc: 0.962 - ETA: 0s - loss: 0.1470 - acc: 0.962 - ETA: 0s - loss: 0.1472 - acc: 0.962 - ETA: 0s - loss: 0.1489 - acc: 0.9620Epoch 00001: val_loss did not improve 6680/6680 [==============================] - 7s - loss: 0.1487 - acc: 0.9620 - val_loss: 0.4111 - val_acc: 0.8707 Epoch 3/20 6650/6680 [============================>.] - ETA: 4s - loss: 0.1709 - acc: 0.940 - ETA: 5s - loss: 0.1908 - acc: 0.920 - ETA: 6s - loss: 0.1627 - acc: 0.946 - ETA: 6s - loss: 0.1515 - acc: 0.955 - ETA: 6s - loss: 0.1424 - acc: 0.964 - ETA: 6s - loss: 0.1500 - acc: 0.956 - ETA: 6s - loss: 0.1595 - acc: 0.960 - ETA: 6s - loss: 0.1638 - acc: 0.957 - ETA: 6s - loss: 0.1581 - acc: 0.957 - ETA: 6s - loss: 0.1567 - acc: 0.960 - ETA: 6s - loss: 0.1563 - acc: 0.960 - ETA: 6s - loss: 0.1500 - acc: 0.963 - ETA: 6s - loss: 0.1489 - acc: 0.964 - ETA: 6s - loss: 0.1445 - acc: 0.967 - ETA: 6s - loss: 0.1439 - acc: 0.968 - ETA: 6s - loss: 0.1396 - acc: 0.968 - ETA: 6s - loss: 0.1398 - acc: 0.969 - ETA: 6s - loss: 0.1379 - acc: 0.971 - ETA: 6s - loss: 0.1377 - acc: 0.970 - ETA: 6s - loss: 0.1372 - acc: 0.971 - ETA: 6s - loss: 0.1449 - acc: 0.967 - ETA: 5s - loss: 0.1446 - acc: 0.967 - ETA: 5s - loss: 0.1475 - acc: 0.964 - ETA: 5s - loss: 0.1508 - acc: 0.962 - ETA: 5s - loss: 0.1550 - acc: 0.961 - ETA: 5s - loss: 0.1516 - acc: 0.962 - ETA: 5s - loss: 0.1525 - acc: 0.962 - ETA: 5s - loss: 0.1512 - acc: 0.963 - ETA: 5s - loss: 0.1536 - acc: 0.961 - ETA: 5s - loss: 0.1522 - acc: 0.962 - ETA: 5s - loss: 0.1511 - acc: 0.962 - ETA: 5s - loss: 0.1508 - acc: 0.962 - ETA: 5s - loss: 0.1498 - acc: 0.962 - ETA: 5s - loss: 0.1485 - acc: 0.963 - ETA: 5s - loss: 0.1467 - acc: 0.964 - ETA: 5s - loss: 0.1465 - acc: 0.965 - ETA: 4s - loss: 0.1451 - acc: 0.965 - ETA: 4s - loss: 0.1425 - acc: 0.967 - ETA: 4s - loss: 0.1417 - acc: 0.967 - ETA: 4s - loss: 0.1424 - acc: 0.967 - ETA: 4s - loss: 0.1431 - acc: 0.966 - ETA: 4s - loss: 0.1422 - acc: 0.967 - ETA: 4s - loss: 0.1444 - acc: 0.966 - ETA: 4s - loss: 0.1438 - acc: 0.966 - ETA: 4s - loss: 0.1451 - acc: 0.966 - ETA: 4s - loss: 0.1438 - acc: 0.966 - ETA: 4s - loss: 0.1442 - acc: 0.966 - ETA: 4s - loss: 0.1438 - acc: 0.966 - ETA: 4s - loss: 0.1460 - acc: 0.965 - ETA: 4s - loss: 0.1455 - acc: 0.966 - ETA: 4s - loss: 0.1453 - acc: 0.966 - ETA: 4s - loss: 0.1445 - acc: 0.967 - ETA: 4s - loss: 0.1420 - acc: 0.968 - 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acc: 0.969 - ETA: 2s - loss: 0.1379 - acc: 0.969 - ETA: 2s - loss: 0.1376 - acc: 0.969 - ETA: 2s - loss: 0.1372 - acc: 0.969 - ETA: 2s - loss: 0.1379 - acc: 0.969 - ETA: 2s - loss: 0.1387 - acc: 0.968 - ETA: 2s - loss: 0.1380 - acc: 0.968 - ETA: 1s - loss: 0.1387 - acc: 0.968 - ETA: 1s - loss: 0.1386 - acc: 0.968 - ETA: 1s - loss: 0.1393 - acc: 0.967 - ETA: 1s - loss: 0.1393 - acc: 0.967 - ETA: 1s - loss: 0.1393 - acc: 0.967 - ETA: 1s - loss: 0.1388 - acc: 0.967 - ETA: 1s - loss: 0.1388 - acc: 0.967 - ETA: 1s - loss: 0.1392 - acc: 0.967 - ETA: 1s - loss: 0.1392 - acc: 0.967 - ETA: 1s - loss: 0.1394 - acc: 0.966 - ETA: 1s - loss: 0.1392 - acc: 0.966 - ETA: 1s - loss: 0.1393 - acc: 0.966 - ETA: 1s - loss: 0.1389 - acc: 0.966 - ETA: 1s - loss: 0.1391 - acc: 0.966 - ETA: 1s - loss: 0.1393 - acc: 0.967 - ETA: 1s - loss: 0.1392 - acc: 0.966 - ETA: 1s - loss: 0.1392 - acc: 0.966 - ETA: 1s - loss: 0.1393 - acc: 0.966 - ETA: 0s - loss: 0.1397 - acc: 0.966 - ETA: 0s - loss: 0.1409 - acc: 0.966 - ETA: 0s - loss: 0.1410 - acc: 0.966 - ETA: 0s - loss: 0.1405 - acc: 0.966 - ETA: 0s - loss: 0.1402 - acc: 0.966 - ETA: 0s - loss: 0.1412 - acc: 0.965 - ETA: 0s - loss: 0.1407 - acc: 0.966 - ETA: 0s - loss: 0.1410 - acc: 0.965 - ETA: 0s - loss: 0.1407 - acc: 0.966 - ETA: 0s - loss: 0.1412 - acc: 0.965 - ETA: 0s - loss: 0.1408 - acc: 0.965 - ETA: 0s - loss: 0.1406 - acc: 0.965 - ETA: 0s - loss: 0.1412 - acc: 0.965 - ETA: 0s - loss: 0.1425 - acc: 0.965 - ETA: 0s - loss: 0.1433 - acc: 0.964 - ETA: 0s - loss: 0.1429 - acc: 0.9647Epoch 00002: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.1427 - acc: 0.9648 - val_loss: 0.4133 - val_acc: 0.8635 Epoch 4/20 6650/6680 [============================>.] - ETA: 4s - loss: 0.1730 - acc: 0.960 - ETA: 5s - loss: 0.1495 - acc: 0.960 - ETA: 5s - loss: 0.1471 - acc: 0.960 - ETA: 6s - loss: 0.1413 - acc: 0.968 - ETA: 6s - loss: 0.1359 - acc: 0.970 - ETA: 6s - loss: 0.1422 - acc: 0.968 - ETA: 6s - loss: 0.1393 - acc: 0.968 - 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acc: 0.969 - ETA: 5s - loss: 0.1302 - acc: 0.969 - ETA: 4s - loss: 0.1307 - acc: 0.969 - ETA: 4s - loss: 0.1323 - acc: 0.968 - ETA: 4s - loss: 0.1340 - acc: 0.967 - ETA: 4s - loss: 0.1346 - acc: 0.967 - ETA: 4s - loss: 0.1355 - acc: 0.966 - ETA: 4s - loss: 0.1346 - acc: 0.967 - ETA: 4s - loss: 0.1360 - acc: 0.966 - ETA: 4s - loss: 0.1358 - acc: 0.966 - ETA: 4s - loss: 0.1341 - acc: 0.966 - ETA: 4s - loss: 0.1330 - acc: 0.967 - ETA: 4s - loss: 0.1322 - acc: 0.967 - ETA: 4s - loss: 0.1327 - acc: 0.967 - ETA: 4s - loss: 0.1321 - acc: 0.967 - ETA: 4s - loss: 0.1339 - acc: 0.967 - ETA: 4s - loss: 0.1351 - acc: 0.966 - ETA: 4s - loss: 0.1341 - acc: 0.967 - ETA: 4s - loss: 0.1337 - acc: 0.967 - ETA: 4s - loss: 0.1334 - acc: 0.967 - ETA: 4s - loss: 0.1345 - acc: 0.966 - ETA: 4s - loss: 0.1349 - acc: 0.966 - ETA: 3s - loss: 0.1352 - acc: 0.966 - ETA: 3s - loss: 0.1352 - acc: 0.966 - ETA: 3s - loss: 0.1351 - acc: 0.966 - ETA: 3s - loss: 0.1336 - acc: 0.967 - ETA: 3s - loss: 0.1341 - acc: 0.967 - 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acc: 0.965 - ETA: 2s - loss: 0.1396 - acc: 0.965 - ETA: 2s - loss: 0.1394 - acc: 0.965 - ETA: 2s - loss: 0.1398 - acc: 0.964 - ETA: 1s - loss: 0.1394 - acc: 0.965 - ETA: 1s - loss: 0.1393 - acc: 0.965 - ETA: 1s - loss: 0.1398 - acc: 0.965 - ETA: 1s - loss: 0.1397 - acc: 0.965 - ETA: 1s - loss: 0.1393 - acc: 0.965 - ETA: 1s - loss: 0.1390 - acc: 0.965 - ETA: 1s - loss: 0.1398 - acc: 0.965 - ETA: 1s - loss: 0.1404 - acc: 0.964 - ETA: 1s - loss: 0.1401 - acc: 0.964 - ETA: 1s - loss: 0.1402 - acc: 0.965 - ETA: 1s - loss: 0.1407 - acc: 0.964 - ETA: 1s - loss: 0.1403 - acc: 0.965 - ETA: 1s - loss: 0.1393 - acc: 0.965 - ETA: 1s - loss: 0.1389 - acc: 0.965 - ETA: 1s - loss: 0.1390 - acc: 0.965 - ETA: 0s - loss: 0.1387 - acc: 0.965 - ETA: 0s - loss: 0.1383 - acc: 0.966 - ETA: 0s - loss: 0.1392 - acc: 0.965 - ETA: 0s - loss: 0.1387 - acc: 0.965 - ETA: 0s - loss: 0.1389 - acc: 0.965 - ETA: 0s - loss: 0.1385 - acc: 0.966 - ETA: 0s - loss: 0.1379 - acc: 0.966 - ETA: 0s - loss: 0.1382 - acc: 0.966 - ETA: 0s - loss: 0.1378 - acc: 0.966 - ETA: 0s - loss: 0.1379 - acc: 0.966 - ETA: 0s - loss: 0.1375 - acc: 0.966 - ETA: 0s - loss: 0.1374 - acc: 0.966 - ETA: 0s - loss: 0.1372 - acc: 0.966 - ETA: 0s - loss: 0.1372 - acc: 0.966 - ETA: 0s - loss: 0.1373 - acc: 0.966 - ETA: 0s - loss: 0.1373 - acc: 0.966 - ETA: 0s - loss: 0.1372 - acc: 0.9665Epoch 00003: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.1370 - acc: 0.9666 - val_loss: 0.4131 - val_acc: 0.8659 Epoch 5/20 6650/6680 [============================>.] - ETA: 5s - loss: 0.1775 - acc: 0.960 - ETA: 5s - loss: 0.1365 - acc: 0.970 - ETA: 5s - loss: 0.1306 - acc: 0.975 - ETA: 6s - loss: 0.1332 - acc: 0.968 - ETA: 6s - loss: 0.1355 - acc: 0.970 - ETA: 6s - loss: 0.1376 - acc: 0.971 - ETA: 6s - loss: 0.1351 - acc: 0.972 - ETA: 6s - loss: 0.1407 - acc: 0.973 - ETA: 6s - loss: 0.1339 - acc: 0.976 - ETA: 6s - loss: 0.1336 - acc: 0.974 - ETA: 6s - loss: 0.1293 - acc: 0.976 - ETA: 6s - loss: 0.1359 - acc: 0.971 - 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acc: 0.970 - ETA: 1s - loss: 0.1244 - acc: 0.970 - ETA: 1s - loss: 0.1242 - acc: 0.970 - ETA: 1s - loss: 0.1239 - acc: 0.970 - ETA: 1s - loss: 0.1242 - acc: 0.971 - ETA: 1s - loss: 0.1253 - acc: 0.971 - ETA: 1s - loss: 0.1254 - acc: 0.971 - ETA: 1s - loss: 0.1254 - acc: 0.971 - ETA: 1s - loss: 0.1257 - acc: 0.970 - ETA: 1s - loss: 0.1262 - acc: 0.970 - ETA: 1s - loss: 0.1265 - acc: 0.970 - ETA: 1s - loss: 0.1270 - acc: 0.970 - ETA: 1s - loss: 0.1264 - acc: 0.970 - ETA: 0s - loss: 0.1259 - acc: 0.970 - ETA: 0s - loss: 0.1254 - acc: 0.970 - ETA: 0s - loss: 0.1254 - acc: 0.971 - ETA: 0s - loss: 0.1258 - acc: 0.970 - ETA: 0s - loss: 0.1259 - acc: 0.970 - ETA: 0s - loss: 0.1259 - acc: 0.970 - ETA: 0s - loss: 0.1263 - acc: 0.970 - ETA: 0s - loss: 0.1264 - acc: 0.970 - ETA: 0s - loss: 0.1262 - acc: 0.970 - ETA: 0s - loss: 0.1259 - acc: 0.970 - ETA: 0s - loss: 0.1260 - acc: 0.971 - ETA: 0s - loss: 0.1265 - acc: 0.970 - ETA: 0s - loss: 0.1265 - acc: 0.970 - ETA: 0s - loss: 0.1263 - acc: 0.970 - 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ETA: 0s - loss: 0.1230 - acc: 0.971 - ETA: 0s - loss: 0.1230 - acc: 0.971 - ETA: 0s - loss: 0.1226 - acc: 0.971 - ETA: 0s - loss: 0.1224 - acc: 0.971 - ETA: 0s - loss: 0.1221 - acc: 0.972 - ETA: 0s - loss: 0.1219 - acc: 0.972 - ETA: 0s - loss: 0.1214 - acc: 0.972 - ETA: 0s - loss: 0.1219 - acc: 0.972 - ETA: 0s - loss: 0.1218 - acc: 0.9723Epoch 00006: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.1223 - acc: 0.9723 - val_loss: 0.4101 - val_acc: 0.8623 Epoch 8/20 6650/6680 [============================>.] - ETA: 7s - loss: 0.1903 - acc: 0.940 - ETA: 7s - loss: 0.1786 - acc: 0.940 - ETA: 7s - loss: 0.1387 - acc: 0.960 - ETA: 7s - loss: 0.1365 - acc: 0.960 - ETA: 7s - loss: 0.1313 - acc: 0.960 - ETA: 7s - loss: 0.1327 - acc: 0.963 - ETA: 7s - loss: 0.1218 - acc: 0.968 - ETA: 7s - loss: 0.1302 - acc: 0.965 - ETA: 7s - loss: 0.1230 - acc: 0.968 - ETA: 7s - loss: 0.1178 - acc: 0.972 - ETA: 7s - loss: 0.1138 - acc: 0.972 - ETA: 6s - loss: 0.1107 - acc: 0.973 - 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val_acc: 0.8647 Epoch 11/20 6650/6680 [============================>.] - ETA: 5s - loss: 0.1036 - acc: 0.980 - ETA: 6s - loss: 0.0874 - acc: 0.990 - ETA: 6s - loss: 0.0979 - acc: 0.986 - ETA: 6s - loss: 0.0931 - acc: 0.990 - ETA: 6s - loss: 0.0961 - acc: 0.984 - ETA: 6s - loss: 0.0926 - acc: 0.986 - ETA: 6s - loss: 0.0953 - acc: 0.985 - ETA: 6s - loss: 0.0975 - acc: 0.985 - ETA: 6s - loss: 0.0969 - acc: 0.986 - ETA: 6s - loss: 0.0933 - acc: 0.988 - ETA: 6s - loss: 0.0939 - acc: 0.987 - ETA: 6s - loss: 0.0931 - acc: 0.986 - ETA: 6s - loss: 0.0973 - acc: 0.984 - ETA: 6s - loss: 0.0965 - acc: 0.984 - ETA: 6s - loss: 0.0934 - acc: 0.985 - ETA: 6s - loss: 0.0996 - acc: 0.981 - ETA: 6s - loss: 0.0976 - acc: 0.982 - ETA: 6s - loss: 0.0981 - acc: 0.981 - ETA: 6s - loss: 0.0960 - acc: 0.981 - ETA: 6s - loss: 0.0960 - acc: 0.981 - ETA: 6s - loss: 0.0955 - acc: 0.981 - ETA: 6s - loss: 0.0964 - acc: 0.981 - ETA: 6s - loss: 0.0979 - acc: 0.980 - ETA: 6s - loss: 0.0978 - acc: 0.979 - ETA: 6s - loss: 0.0977 - 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acc: 0.977 - ETA: 0s - loss: 0.1064 - acc: 0.9776Epoch 00010: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.1063 - acc: 0.9775 - val_loss: 0.4103 - val_acc: 0.8695 Epoch 12/20 6650/6680 [============================>.] - ETA: 5s - loss: 0.1517 - acc: 0.960 - ETA: 6s - loss: 0.1324 - acc: 0.970 - ETA: 6s - loss: 0.1136 - acc: 0.966 - ETA: 6s - loss: 0.1006 - acc: 0.975 - ETA: 6s - loss: 0.1018 - acc: 0.976 - ETA: 7s - loss: 0.0950 - acc: 0.980 - ETA: 6s - loss: 0.1000 - acc: 0.980 - ETA: 6s - loss: 0.1090 - acc: 0.977 - ETA: 6s - loss: 0.1096 - acc: 0.977 - ETA: 6s - loss: 0.1068 - acc: 0.978 - ETA: 6s - loss: 0.1027 - acc: 0.980 - ETA: 6s - loss: 0.1017 - acc: 0.981 - ETA: 7s - loss: 0.1043 - acc: 0.980 - ETA: 6s - loss: 0.1058 - acc: 0.978 - ETA: 6s - loss: 0.1065 - acc: 0.977 - ETA: 6s - loss: 0.1054 - acc: 0.977 - ETA: 6s - loss: 0.1046 - acc: 0.978 - ETA: 6s - loss: 0.1050 - acc: 0.977 - ETA: 6s - loss: 0.1055 - acc: 0.977 - ETA: 6s - loss: 0.1047 - 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acc: 0.978 - ETA: 0s - loss: 0.1026 - acc: 0.978 - ETA: 0s - loss: 0.1030 - acc: 0.978 - ETA: 0s - loss: 0.1034 - acc: 0.978 - ETA: 0s - loss: 0.1032 - acc: 0.978 - ETA: 0s - loss: 0.1029 - acc: 0.978 - ETA: 0s - loss: 0.1031 - acc: 0.9783Epoch 00011: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.1030 - acc: 0.9783 - val_loss: 0.4154 - val_acc: 0.8635 Epoch 13/20 6650/6680 [============================>.] - ETA: 6s - loss: 0.1133 - acc: 0.940 - ETA: 6s - loss: 0.0860 - acc: 0.980 - ETA: 6s - loss: 0.0932 - acc: 0.970 - ETA: 6s - loss: 0.0868 - acc: 0.976 - ETA: 6s - loss: 0.0855 - acc: 0.980 - ETA: 6s - loss: 0.0813 - acc: 0.982 - ETA: 7s - loss: 0.0803 - acc: 0.982 - ETA: 7s - loss: 0.0790 - acc: 0.982 - ETA: 7s - loss: 0.0824 - acc: 0.982 - ETA: 6s - loss: 0.0808 - acc: 0.981 - ETA: 6s - loss: 0.0798 - acc: 0.981 - ETA: 6s - loss: 0.0765 - acc: 0.983 - ETA: 6s - loss: 0.0782 - acc: 0.984 - ETA: 6s - loss: 0.0802 - acc: 0.982 - ETA: 6s - loss: 0.0794 - 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acc: 0.978 - ETA: 0s - loss: 0.0984 - acc: 0.978 - ETA: 0s - loss: 0.0985 - acc: 0.978 - ETA: 0s - loss: 0.0989 - acc: 0.978 - ETA: 0s - loss: 0.0987 - acc: 0.978 - ETA: 0s - loss: 0.0988 - acc: 0.978 - ETA: 0s - loss: 0.0990 - acc: 0.978 - ETA: 0s - loss: 0.0988 - acc: 0.978 - ETA: 0s - loss: 0.0991 - acc: 0.978 - ETA: 0s - loss: 0.0991 - acc: 0.978 - ETA: 0s - loss: 0.0995 - acc: 0.977 - ETA: 0s - loss: 0.0997 - acc: 0.9777Epoch 00012: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.0997 - acc: 0.9777 - val_loss: 0.4111 - val_acc: 0.8659 Epoch 14/20 6650/6680 [============================>.] - ETA: 6s - loss: 0.0879 - acc: 1.000 - ETA: 6s - loss: 0.0714 - acc: 1.000 - ETA: 7s - loss: 0.0881 - acc: 0.986 - ETA: 7s - loss: 0.0911 - acc: 0.985 - ETA: 7s - loss: 0.0901 - acc: 0.984 - ETA: 7s - loss: 0.0840 - acc: 0.983 - ETA: 7s - loss: 0.1045 - acc: 0.982 - ETA: 7s - loss: 0.0976 - acc: 0.985 - ETA: 7s - loss: 0.0989 - acc: 0.984 - ETA: 7s - loss: 0.0956 - 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acc: 0.980 - ETA: 0s - loss: 0.0947 - acc: 0.980 - ETA: 0s - loss: 0.0945 - acc: 0.980 - ETA: 0s - loss: 0.0954 - acc: 0.980 - ETA: 0s - loss: 0.0956 - acc: 0.980 - ETA: 0s - loss: 0.0952 - acc: 0.980 - ETA: 0s - loss: 0.0953 - acc: 0.980 - ETA: 0s - loss: 0.0955 - acc: 0.980 - ETA: 0s - loss: 0.0953 - acc: 0.980 - ETA: 0s - loss: 0.0951 - acc: 0.980 - ETA: 0s - loss: 0.0951 - acc: 0.980 - ETA: 0s - loss: 0.0953 - acc: 0.980 - ETA: 0s - loss: 0.0956 - acc: 0.980 - ETA: 0s - loss: 0.0953 - acc: 0.980 - ETA: 0s - loss: 0.0954 - acc: 0.980 - ETA: 0s - loss: 0.0957 - acc: 0.980 - ETA: 0s - loss: 0.0968 - acc: 0.980 - ETA: 0s - loss: 0.0965 - acc: 0.9805Epoch 00013: val_loss improved from 0.40994 to 0.40853, saving model to saved_models/weights.best.Xception.hdf5 6680/6680 [==============================] - 8s - loss: 0.0963 - acc: 0.9805 - val_loss: 0.4085 - val_acc: 0.8695 Epoch 15/20 6650/6680 [============================>.] - ETA: 5s - loss: 0.0678 - acc: 0.980 - ETA: 6s - loss: 0.0898 - 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acc: 0.982 - ETA: 1s - loss: 0.0908 - acc: 0.982 - ETA: 1s - loss: 0.0906 - acc: 0.983 - ETA: 1s - loss: 0.0908 - acc: 0.982 - ETA: 1s - loss: 0.0910 - acc: 0.982 - ETA: 1s - loss: 0.0911 - acc: 0.982 - ETA: 1s - loss: 0.0908 - acc: 0.982 - ETA: 0s - loss: 0.0905 - acc: 0.982 - ETA: 0s - loss: 0.0905 - acc: 0.982 - ETA: 0s - loss: 0.0909 - acc: 0.982 - ETA: 0s - loss: 0.0914 - acc: 0.982 - ETA: 0s - loss: 0.0911 - acc: 0.982 - ETA: 0s - loss: 0.0912 - acc: 0.982 - ETA: 0s - loss: 0.0911 - acc: 0.982 - ETA: 0s - loss: 0.0914 - acc: 0.982 - ETA: 0s - loss: 0.0920 - acc: 0.982 - ETA: 0s - loss: 0.0916 - acc: 0.982 - ETA: 0s - loss: 0.0924 - acc: 0.982 - ETA: 0s - loss: 0.0924 - acc: 0.982 - ETA: 0s - loss: 0.0929 - acc: 0.982 - ETA: 0s - loss: 0.0930 - acc: 0.982 - ETA: 0s - loss: 0.0931 - acc: 0.982 - ETA: 0s - loss: 0.0928 - acc: 0.982 - ETA: 0s - loss: 0.0929 - acc: 0.9820Epoch 00014: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.0932 - acc: 0.9819 - val_loss: 0.4120 - val_acc: 0.8611 Epoch 16/20 6650/6680 [============================>.] - ETA: 7s - loss: 0.1248 - acc: 0.940 - ETA: 7s - loss: 0.0918 - acc: 0.970 - ETA: 7s - loss: 0.0947 - acc: 0.966 - ETA: 7s - loss: 0.1041 - acc: 0.960 - ETA: 7s - loss: 0.1014 - acc: 0.964 - ETA: 7s - loss: 0.0921 - acc: 0.970 - ETA: 7s - loss: 0.0873 - acc: 0.974 - ETA: 7s - loss: 0.0882 - acc: 0.975 - ETA: 7s - loss: 0.0873 - acc: 0.975 - ETA: 7s - loss: 0.0894 - acc: 0.974 - ETA: 7s - loss: 0.0877 - acc: 0.976 - ETA: 7s - loss: 0.0883 - acc: 0.976 - ETA: 7s - loss: 0.0867 - acc: 0.976 - ETA: 7s - loss: 0.0907 - acc: 0.975 - ETA: 6s - loss: 0.0921 - acc: 0.976 - ETA: 6s - loss: 0.0917 - acc: 0.976 - ETA: 6s - loss: 0.0921 - acc: 0.975 - ETA: 6s - loss: 0.0958 - acc: 0.975 - ETA: 6s - loss: 0.0925 - acc: 0.976 - ETA: 6s - loss: 0.0900 - acc: 0.978 - ETA: 6s - loss: 0.0913 - acc: 0.977 - ETA: 6s - loss: 0.0908 - acc: 0.977 - ETA: 6s - loss: 0.0904 - acc: 0.977 - ETA: 6s - loss: 0.0894 - acc: 0.977 - 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acc: 0.982 - ETA: 1s - loss: 0.0896 - acc: 0.982 - ETA: 1s - loss: 0.0895 - acc: 0.982 - ETA: 1s - loss: 0.0892 - acc: 0.983 - ETA: 1s - loss: 0.0895 - acc: 0.982 - ETA: 1s - loss: 0.0897 - acc: 0.982 - ETA: 1s - loss: 0.0893 - acc: 0.982 - ETA: 1s - loss: 0.0891 - acc: 0.983 - ETA: 1s - loss: 0.0892 - acc: 0.983 - ETA: 1s - loss: 0.0894 - acc: 0.983 - ETA: 1s - loss: 0.0892 - acc: 0.983 - ETA: 1s - loss: 0.0896 - acc: 0.983 - ETA: 1s - loss: 0.0900 - acc: 0.982 - ETA: 0s - loss: 0.0898 - acc: 0.982 - ETA: 0s - loss: 0.0898 - acc: 0.982 - ETA: 0s - loss: 0.0898 - acc: 0.982 - ETA: 0s - loss: 0.0901 - acc: 0.982 - ETA: 0s - loss: 0.0916 - acc: 0.982 - ETA: 0s - loss: 0.0912 - acc: 0.982 - ETA: 0s - loss: 0.0909 - acc: 0.982 - ETA: 0s - loss: 0.0911 - acc: 0.982 - ETA: 0s - loss: 0.0912 - acc: 0.982 - ETA: 0s - loss: 0.0908 - acc: 0.982 - ETA: 0s - loss: 0.0907 - acc: 0.982 - ETA: 0s - loss: 0.0909 - acc: 0.982 - ETA: 0s - loss: 0.0906 - acc: 0.982 - ETA: 0s - loss: 0.0903 - acc: 0.982 - ETA: 0s - loss: 0.0903 - acc: 0.982 - ETA: 0s - loss: 0.0902 - acc: 0.982 - ETA: 0s - loss: 0.0901 - acc: 0.9827Epoch 00015: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.0900 - acc: 0.9828 - val_loss: 0.4161 - val_acc: 0.8599 Epoch 17/20 6650/6680 [============================>.] - ETA: 5s - loss: 0.0283 - acc: 1.000 - ETA: 6s - loss: 0.0524 - acc: 1.000 - ETA: 6s - loss: 0.0592 - acc: 1.000 - ETA: 7s - loss: 0.0538 - acc: 1.000 - ETA: 7s - loss: 0.0589 - acc: 0.996 - ETA: 7s - loss: 0.0599 - acc: 0.996 - ETA: 7s - loss: 0.0658 - acc: 0.994 - ETA: 7s - loss: 0.0653 - acc: 0.995 - ETA: 7s - loss: 0.0645 - acc: 0.995 - ETA: 7s - loss: 0.0645 - acc: 0.994 - ETA: 7s - loss: 0.0680 - acc: 0.992 - ETA: 7s - loss: 0.0707 - acc: 0.991 - ETA: 6s - loss: 0.0752 - acc: 0.989 - ETA: 6s - loss: 0.0735 - acc: 0.990 - ETA: 6s - loss: 0.0779 - acc: 0.988 - ETA: 6s - loss: 0.0759 - acc: 0.988 - ETA: 6s - loss: 0.0766 - acc: 0.989 - ETA: 6s - loss: 0.0752 - acc: 0.990 - 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ETA: 0s - loss: 0.0872 - acc: 0.981 - ETA: 0s - loss: 0.0871 - acc: 0.981 - ETA: 0s - loss: 0.0871 - acc: 0.981 - ETA: 0s - loss: 0.0872 - acc: 0.981 - ETA: 0s - loss: 0.0872 - acc: 0.981 - ETA: 0s - loss: 0.0870 - acc: 0.981 - ETA: 0s - loss: 0.0876 - acc: 0.981 - ETA: 0s - loss: 0.0874 - acc: 0.981 - ETA: 0s - loss: 0.0872 - acc: 0.9817Epoch 00016: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.0875 - acc: 0.9814 - val_loss: 0.4172 - val_acc: 0.8659 Epoch 18/20 6650/6680 [============================>.] - ETA: 6s - loss: 0.0716 - acc: 1.000 - ETA: 7s - loss: 0.0616 - acc: 1.000 - ETA: 6s - loss: 0.0581 - acc: 1.000 - ETA: 7s - loss: 0.0633 - acc: 1.000 - ETA: 7s - loss: 0.0729 - acc: 0.996 - ETA: 7s - loss: 0.0867 - acc: 0.990 - ETA: 7s - loss: 0.0965 - acc: 0.985 - ETA: 7s - loss: 0.0884 - acc: 0.987 - ETA: 7s - loss: 0.0868 - acc: 0.988 - ETA: 6s - loss: 0.0842 - acc: 0.990 - ETA: 6s - loss: 0.0832 - acc: 0.989 - ETA: 6s - loss: 0.0801 - acc: 0.990 - 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acc: 0.983 - ETA: 2s - loss: 0.0854 - acc: 0.983 - ETA: 2s - loss: 0.0858 - acc: 0.983 - ETA: 2s - loss: 0.0858 - acc: 0.983 - ETA: 2s - loss: 0.0854 - acc: 0.983 - ETA: 2s - loss: 0.0851 - acc: 0.983 - ETA: 2s - loss: 0.0853 - acc: 0.983 - ETA: 2s - loss: 0.0853 - acc: 0.983 - ETA: 2s - loss: 0.0850 - acc: 0.983 - ETA: 1s - loss: 0.0848 - acc: 0.983 - ETA: 1s - loss: 0.0846 - acc: 0.983 - ETA: 1s - loss: 0.0845 - acc: 0.983 - ETA: 1s - loss: 0.0847 - acc: 0.983 - ETA: 1s - loss: 0.0846 - acc: 0.983 - ETA: 1s - loss: 0.0844 - acc: 0.983 - ETA: 1s - loss: 0.0842 - acc: 0.983 - ETA: 1s - loss: 0.0841 - acc: 0.983 - ETA: 1s - loss: 0.0843 - acc: 0.983 - ETA: 1s - loss: 0.0846 - acc: 0.983 - ETA: 1s - loss: 0.0850 - acc: 0.983 - ETA: 1s - loss: 0.0847 - acc: 0.983 - ETA: 1s - loss: 0.0844 - acc: 0.983 - ETA: 1s - loss: 0.0841 - acc: 0.983 - ETA: 1s - loss: 0.0842 - acc: 0.983 - ETA: 1s - loss: 0.0846 - acc: 0.983 - ETA: 1s - loss: 0.0843 - acc: 0.983 - ETA: 0s - loss: 0.0846 - acc: 0.983 - ETA: 0s - loss: 0.0846 - acc: 0.983 - ETA: 0s - loss: 0.0844 - acc: 0.983 - ETA: 0s - loss: 0.0844 - acc: 0.983 - ETA: 0s - loss: 0.0852 - acc: 0.983 - ETA: 0s - loss: 0.0850 - acc: 0.983 - ETA: 0s - loss: 0.0848 - acc: 0.983 - ETA: 0s - loss: 0.0847 - acc: 0.983 - ETA: 0s - loss: 0.0857 - acc: 0.982 - ETA: 0s - loss: 0.0859 - acc: 0.982 - ETA: 0s - loss: 0.0856 - acc: 0.982 - ETA: 0s - loss: 0.0855 - acc: 0.982 - ETA: 0s - loss: 0.0853 - acc: 0.982 - ETA: 0s - loss: 0.0850 - acc: 0.983 - ETA: 0s - loss: 0.0847 - acc: 0.983 - ETA: 0s - loss: 0.0847 - acc: 0.9832Epoch 00017: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.0845 - acc: 0.9832 - val_loss: 0.4161 - val_acc: 0.8623 Epoch 19/20 6650/6680 [============================>.] - ETA: 6s - loss: 0.0559 - acc: 1.000 - ETA: 6s - loss: 0.0488 - acc: 1.000 - ETA: 7s - loss: 0.0565 - acc: 1.000 - ETA: 7s - loss: 0.0668 - acc: 0.990 - ETA: 7s - loss: 0.0641 - acc: 0.992 - ETA: 7s - loss: 0.0609 - acc: 0.990 - ETA: 7s - loss: 0.0604 - acc: 0.991 - ETA: 7s - loss: 0.0586 - acc: 0.990 - ETA: 7s - loss: 0.0594 - acc: 0.988 - ETA: 7s - loss: 0.0603 - acc: 0.990 - ETA: 7s - loss: 0.0626 - acc: 0.989 - ETA: 7s - loss: 0.0623 - acc: 0.990 - ETA: 7s - loss: 0.0631 - acc: 0.989 - ETA: 7s - loss: 0.0651 - acc: 0.990 - ETA: 6s - loss: 0.0671 - acc: 0.989 - ETA: 6s - loss: 0.0667 - acc: 0.988 - ETA: 6s - loss: 0.0689 - acc: 0.989 - ETA: 6s - loss: 0.0705 - acc: 0.990 - ETA: 6s - loss: 0.0699 - acc: 0.990 - ETA: 6s - loss: 0.0685 - acc: 0.991 - ETA: 6s - loss: 0.0707 - acc: 0.989 - ETA: 6s - loss: 0.0703 - acc: 0.990 - ETA: 6s - loss: 0.0694 - acc: 0.990 - ETA: 6s - loss: 0.0678 - acc: 0.990 - ETA: 6s - loss: 0.0702 - acc: 0.990 - ETA: 6s - loss: 0.0713 - acc: 0.990 - ETA: 6s - loss: 0.0708 - acc: 0.990 - ETA: 6s - loss: 0.0708 - acc: 0.990 - ETA: 6s - loss: 0.0704 - acc: 0.990 - ETA: 6s - loss: 0.0691 - acc: 0.990 - ETA: 6s - loss: 0.0698 - acc: 0.990 - ETA: 6s - loss: 0.0697 - acc: 0.990 - ETA: 5s - loss: 0.0691 - 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acc: 0.986 - ETA: 2s - loss: 0.0787 - acc: 0.986 - ETA: 2s - loss: 0.0782 - acc: 0.986 - ETA: 2s - loss: 0.0779 - acc: 0.986 - ETA: 2s - loss: 0.0780 - acc: 0.986 - ETA: 2s - loss: 0.0777 - acc: 0.985 - ETA: 2s - loss: 0.0775 - acc: 0.986 - ETA: 2s - loss: 0.0775 - acc: 0.986 - ETA: 2s - loss: 0.0776 - acc: 0.986 - ETA: 2s - loss: 0.0781 - acc: 0.985 - ETA: 2s - loss: 0.0780 - acc: 0.986 - ETA: 2s - loss: 0.0783 - acc: 0.985 - ETA: 2s - loss: 0.0787 - acc: 0.985 - ETA: 2s - loss: 0.0794 - acc: 0.985 - ETA: 2s - loss: 0.0789 - acc: 0.985 - ETA: 2s - loss: 0.0789 - acc: 0.985 - ETA: 1s - loss: 0.0787 - acc: 0.985 - ETA: 1s - loss: 0.0786 - acc: 0.986 - ETA: 1s - loss: 0.0784 - acc: 0.986 - ETA: 1s - loss: 0.0780 - acc: 0.986 - ETA: 1s - loss: 0.0782 - acc: 0.986 - ETA: 1s - loss: 0.0781 - acc: 0.986 - ETA: 1s - loss: 0.0786 - acc: 0.985 - ETA: 1s - loss: 0.0786 - acc: 0.985 - ETA: 1s - loss: 0.0784 - acc: 0.985 - ETA: 1s - loss: 0.0782 - acc: 0.985 - ETA: 1s - loss: 0.0782 - acc: 0.985 - ETA: 1s - loss: 0.0784 - acc: 0.985 - ETA: 1s - loss: 0.0788 - acc: 0.985 - ETA: 1s - loss: 0.0787 - acc: 0.985 - ETA: 1s - loss: 0.0787 - acc: 0.985 - ETA: 1s - loss: 0.0785 - acc: 0.985 - ETA: 0s - loss: 0.0792 - acc: 0.985 - ETA: 0s - loss: 0.0797 - acc: 0.985 - ETA: 0s - loss: 0.0796 - acc: 0.985 - ETA: 0s - loss: 0.0799 - acc: 0.985 - ETA: 0s - loss: 0.0802 - acc: 0.984 - ETA: 0s - loss: 0.0804 - acc: 0.984 - ETA: 0s - loss: 0.0802 - acc: 0.985 - ETA: 0s - loss: 0.0800 - acc: 0.985 - ETA: 0s - loss: 0.0802 - acc: 0.984 - ETA: 0s - loss: 0.0801 - acc: 0.984 - ETA: 0s - loss: 0.0805 - acc: 0.984 - ETA: 0s - loss: 0.0813 - acc: 0.984 - ETA: 0s - loss: 0.0811 - acc: 0.984 - ETA: 0s - loss: 0.0813 - acc: 0.984 - ETA: 0s - loss: 0.0814 - acc: 0.984 - ETA: 0s - loss: 0.0823 - acc: 0.9838Epoch 00018: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.0821 - acc: 0.9838 - val_loss: 0.4183 - val_acc: 0.8623 Epoch 20/20 6650/6680 [============================>.] - 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ETA: 1s - loss: 0.0787 - acc: 0.985 - ETA: 1s - loss: 0.0786 - acc: 0.985 - ETA: 1s - loss: 0.0784 - acc: 0.985 - ETA: 1s - loss: 0.0788 - acc: 0.985 - ETA: 1s - loss: 0.0790 - acc: 0.985 - ETA: 1s - loss: 0.0792 - acc: 0.985 - ETA: 1s - loss: 0.0795 - acc: 0.985 - ETA: 1s - loss: 0.0797 - acc: 0.984 - ETA: 1s - loss: 0.0795 - acc: 0.984 - ETA: 1s - loss: 0.0793 - acc: 0.985 - ETA: 1s - loss: 0.0793 - acc: 0.985 - ETA: 0s - loss: 0.0796 - acc: 0.984 - ETA: 0s - loss: 0.0796 - acc: 0.984 - ETA: 0s - loss: 0.0797 - acc: 0.984 - ETA: 0s - loss: 0.0796 - acc: 0.984 - ETA: 0s - loss: 0.0798 - acc: 0.984 - ETA: 0s - loss: 0.0797 - acc: 0.984 - ETA: 0s - loss: 0.0804 - acc: 0.984 - ETA: 0s - loss: 0.0801 - acc: 0.984 - ETA: 0s - loss: 0.0800 - acc: 0.984 - ETA: 0s - loss: 0.0801 - acc: 0.984 - ETA: 0s - loss: 0.0804 - acc: 0.984 - ETA: 0s - loss: 0.0804 - acc: 0.984 - ETA: 0s - loss: 0.0803 - acc: 0.984 - ETA: 0s - loss: 0.0801 - acc: 0.984 - ETA: 0s - loss: 0.0799 - acc: 0.984 - ETA: 0s - loss: 0.0796 - acc: 0.9845Epoch 00019: val_loss did not improve 6680/6680 [==============================] - 8s - loss: 0.0795 - acc: 0.9846 - val_loss: 0.4179 - val_acc: 0.8599
Out[27]:
<keras.callbacks.History at 0x2cd1fc9ec18>
(IMPLEMENTATION) Load the Model with the Best Validation Loss¶
In [28]:
### TODO: Load the model weights with the best validation loss.
transfer_learning_model.load_weights('saved_models/weights.best.Xception.hdf5')
(IMPLEMENTATION) Test the Model¶
Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.
In [29]:
### TODO: Calculate classification accuracy on the test dataset.
# get index of predicted dog breed for each image in test set
transfer_learning_predictions = [np.argmax(transfer_learning_model.predict(np.expand_dims(feature, axis=0))) for feature in test_Xception]
# report test accuracy
test_accuracy = 100*np.sum(np.array(transfer_learning_predictions)==np.argmax(test_targets, axis=1))/len(transfer_learning_predictions)
print('Test accuracy: %.4f%%' % test_accuracy)
Test accuracy: 86.3636%
(IMPLEMENTATION) Predict Dog Breed with the Model¶
Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan_hound, etc) that is predicted by your model.
Similar to the analogous function in Step 5, your function should have three steps:
- Extract the bottleneck features corresponding to the chosen CNN model.
- Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.
- Use the
dog_namesarray defined in Step 0 of this notebook to return the corresponding breed.
The functions to extract the bottleneck features can be found in extract_bottleneck_features.py, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function
extract_{network}
where {network}, in the above filename, should be one of VGG19, Resnet50, InceptionV3, or Xception.
In [81]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
def extract_Xception(tensor):
from keras.applications.xception import Xception, preprocess_input
return Xception(weights='imagenet', include_top=False).predict(preprocess_input(tensor))
def predict_breed(path_to_selected_image):
# pre-process the image
# extract bottleneck features corresponding to Xception model
feature = extract_Xception(path_to_tensor(path_to_selected_image))
# compute predicted dog breed index
dog_likelihoods = transfer_learning_model.predict(feature)
dog_index = np.argmax(dog_likelihoods)
likelihood = dog_likelihoods[-1,dog_index]
#np.expand_dims(feature, axis=0)
# Lookup corresponding Dog description and return both description and likelihood
return dog_names[dog_index], likelihood
Step 6: Write your Algorithm¶
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
- if a dog is detected in the image, return the predicted breed.
- if a human is detected in the image, return the resembling dog breed.
- if neither is detected in the image, provide output that indicates an error.
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and dog_detector functions developed above. You are required to use your CNN from Step 5 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!
(IMPLEMENTATION) Write your Algorithm¶
In [90]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
def show_image(path_to_selected_image):
# load color (BGR) image
img = cv2.imread(path_to_selected_image)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image
plt.imshow(cv_rgb)
plt.show()
return
def show_resembling_breed(path_to_selected_image):
(breed, likelihood) = predict_breed(path_to_selected_image)
if face_detector(path_to_selected_image):
message = 'If you were a dog, I would be %.1f%% positive that you were a ' % (likelihood * 100), breed
elif dog_detector(path_to_selected_image):
message = 'Hello, dog! I am %.1f%% positive that you are a ' % (100 * likelihood) , breed
else:
message = 'You do not look like a human or a dog...'
# show the original picture with text classification
show_image(path_to_selected_image)
print(message)
#TODO: show an example of selected breed
return message
Step 7: Test Your Algorithm¶
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
(IMPLEMENTATION) Test Your Algorithm on Sample Images!¶
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer: Algorithm results in a high accuracy index of over 86% on testing sample, much higher than I expected given there are 133 breeds and only 20 network training epochs. In fact, similarly high accuracy index is attained after the very first training epoch, yielding credit to the selected variation of the gradient descent algorithm.
Also I really like the high confidence scores given to some images of dogs, while low to humans.
In terms of improvements, to make this market ready, I could propose the following steps:
- expand input data by downloading more images and including more breeds, to allow better model accuracy to be achieved
- add random rotations, shifts or zoom in/out to the images to help ensure networks trains in a way that is rotation-invariant.
- consciously decide whether to clean input data to remove pictures of multiple dogs or humans
- adjust network architecture by adding layers before the final dense 'classification' layer - for example instead of global averaging layer, we add one more pair of convolution and max pooling layers
- to make results more interpretable, once the classification is done and confidence score is generated, I would suggest the app to highlight areas of the image that are driving model score to be high - similarly to functionality of highlighting square that is interpteted to be human face in prior demonstration
In [93]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
#show_resembling_breed('images/Pasha.jpg')
#show_resembling_breed('images/Antoine.jpg')
#show_resembling_breed('images/Camille.jpg')
show_resembling_breed('images/American_water_spaniel_00648.jpg')
show_resembling_breed('images/Curly-coated_retriever_03896.jpg')
show_resembling_breed('images/Brittany_02625.jpg')
show_resembling_breed('images/new/dog1.jpg')
show_resembling_breed('images/new/dog2.jpg')
show_resembling_breed('images/new/dog3.jpg')
show_resembling_breed('images/new/putin.jpg')
show_resembling_breed('images/new/trump1.jpg')
show_resembling_breed('images/new/trump2.jpg')
show_resembling_breed('images/new/trump3.jpg')
('Hello, dog! I am 66.5% positive that you are a ', 'Curly-coated_retriever')
('Hello, dog! I am 99.7% positive that you are a ', 'Curly-coated_retriever')
('Hello, dog! I am 98.3% positive that you are a ', 'Brittany')
You do not look like a human or a dog...
('Hello, dog! I am 93.4% positive that you are a ', 'Golden_retriever')
('Hello, dog! I am 96.1% positive that you are a ', 'Alaskan_malamute')
('If you were a dog, I would be 7.6% positive that you were a ', 'Lowchen')
('If you were a dog, I would be 15.7% positive that you were a ', 'Cavalier_king_charles_spaniel')
('If you were a dog, I would be 6.4% positive that you were a ', 'Lowchen')
('If you were a dog, I would be 6.7% positive that you were a ', 'Lowchen')
Out[93]:
('If you were a dog, I would be 6.7% positive that you were a ', 'Lowchen')
In [96]:
# Visualize Lowchen that resembles both Trump and Putin with confidence just over 6%
show_image('dogImages/train/100.Lowchen/Lowchen_06675.jpg')
In [ ]: